Topic evaluation method and related device

Abstract

The invention discloses a speaking topic evaluation method and a related device, and relates to the technical field of data evaluation, and the method comprises the steps: obtaining a topic text and answering voice of a speaking topic, carrying out the voice feature extraction and voice transliteration of the answering voice, and obtaining a voice feature and a voice transliteration text; determining a score of the answer voice in a voice evaluation dimension according to the voice features and the voice transfer text, and determining a score of the answer voice in a semantic evaluation dimension according to the voice transfer text and the question text; duplicate checking is carried out on each sentence of the voice transcription text, and score deduction of the answering voice in the text duplicate checking dimension is determined; and determining a final score of the answer voice according to the score of the answer voice in the voice evaluation dimension, the score of the answer voice in the semantic evaluation dimension and the score deduction of the answer voice in the text duplicate checking dimension. According to the evaluation method disclosed by the invention, the answer voice of the speaking topic can be comprehensively evaluated, and the evaluation result obtained by the evaluation method disclosed by the invention can objectively reflect the ability of the responder.

Description

Technical Field

[0001] The present application relates to the technical field of data evaluation, and in particular to a topic evaluation method and related devices. Background Art

[0002] Topic speaking is widely used in various oral exams and learning scenarios, such as Mandarin proficiency tests, English oral exams, language learning apps, etc. It aims to examine the respondents' oral ability to express themselves around a certain topic without the help of text.

[0003] The current evaluation scheme for speaking topics is to first obtain the respondent's answer voice on the speaking topic, then evaluate the answer voice in the voice evaluation dimension and the semantic evaluation dimension, and obtain the score of the answer voice in the voice evaluation dimension and the score in the semantic evaluation dimension. Finally, according to the score of the answer voice in the voice evaluation dimension and the score in the semantic evaluation dimension, determine the final score of the answer voice.

[0004] Although the current topic speaking evaluation scheme can give evaluation results for the answering voice, its evaluation dimension is not comprehensive enough, which leads to the final evaluation results not being able to objectively reflect the ability of the respondent. Summary of the invention

[0005] In view of this, the present application provides a topic evaluation method and related devices to solve the problem that the evaluation dimensions of the current topic evaluation scheme are not comprehensive enough, which leads to the problem that the final evaluation results cannot objectively reflect the ability of the respondent. The technical solution is as follows:

[0006] The first aspect of the present application provides a topic evaluation method, comprising:

[0007] Acquire the topic text and answer voice of the topic, and perform voice feature extraction and voice transcription on the answer voice to obtain the voice features and voice transcription text of the answer voice;

[0008] Determine the score of the answering speech in the speech evaluation dimension according to the speech features and the speech transcription text, and determine the score of the answering speech in the semantic evaluation dimension according to the speech transcription text and the question text;

[0009] For each sentence of the speech transcription text, the fingerprint vector of the sentence is determined in combination with the position information of each word in the sentence, and the fingerprint vector of the sentence is respectively calculated with the fingerprint vector of each sentence in the duplicate checking text library to determine whether there is a similar sentence of the sentence in the duplicate checking text library, so as to obtain the judgment result corresponding to the sentence; according to the judgment results corresponding to each sentence of the speech transcription text, the deduction of the answer speech in the text duplicate checking dimension is determined;

[0010] The final score of the answering speech is determined based on the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction points in the text duplication checking dimension.

[0011] In a possible implementation, the extracting speech features and transcribing the answering speech to obtain the speech features and speech transcription text of the answering speech includes:

[0012] Extracting speech features of the answering speech to obtain speech features of the answering speech;

[0013] Extracting a voiced speech segment from the answering speech by performing speech endpoint detection on the answering speech;

[0014] The speech features of the voiced speech segment are obtained from the speech features of the answering speech, and speech transcription is performed according to the speech features of the voiced speech segment to obtain a speech transcription text of the answering speech.

[0015] In a possible implementation, the step of determining the fingerprint vector of the sentence in combination with the position information of each word in the sentence includes:

[0016] Perform word segmentation on the sentence to obtain several words;

[0017] For each of the plurality of words, a hash value of the word is determined by using a hash function, and a position feature vector of the word is determined according to the position of the word in the sentence, and a target feature vector of the word is determined according to the hash value of the word and the position feature vector of the word;

[0018] A fingerprint vector of the sentence is determined according to the target feature vectors of the plurality of words.

[0019] In a possible implementation, determining the position feature vector of the word according to the position of the word in the sentence includes:

[0020] Generate a binary vector of length L1 according to the position of the word in the sentence, where L1 is the total number of words contained in the sentence, and the element at each position in the binary vector can indicate whether the word exists at the corresponding position in the sentence;

[0021] Using a mapping function, mapping the binary vector to a vector of length L2 to obtain a mapped vector, wherein L2 is an integer less than L1;

[0022] Calculate the mean of each element in the mapped vector, and subtract the mean from each element in the mapped vector to obtain a mean difference vector;

[0023] The elements in the mean difference vector that are greater than 0 are set to 1, and the elements that are less than or equal to 0 are set to 0, to obtain the position feature vector of the word.

[0024] In a possible implementation, determining the target feature vector of the word according to the hash value of the word and the position feature vector of the word includes:

[0025] The hash value of the word is fused with the position feature vector of the word to obtain the fused vector of the word;

[0026] Determine the weight corresponding to the word according to the number of times the word appears in the sentence, the total number of words contained in the sentence, the total number of words in the duplicate checking text library, and the number of times the word appears in the duplicate checking text library;

[0027] The fused vector of the word is weighted by the weight corresponding to the word to obtain the target feature vector of the word.

[0028] In a possible implementation, determining the fingerprint vector of the sentence according to the target feature vectors of the plurality of words includes:

[0029] Fusing the target feature vectors of the plurality of words to obtain a fused vector;

[0030] The elements in the fused vector that are greater than 0 are set to 1, and the elements that are less than or equal to 0 are set to 0, to obtain the fingerprint vector of the sentence.

[0031] In a possible implementation, the method of calculating the similarity between the fingerprint vector of the sentence and the fingerprint vectors of each sentence in the duplicate checking text library to determine whether there is a similar sentence to the sentence in the duplicate checking text library includes:

[0032] The fingerprint vector of the sentence is respectively calculated with the fingerprint vectors of each sentence in the duplicate checking text library to obtain several similarities;

[0033] Obtaining a maximum similarity from the plurality of similarities;

[0034] If the maximum similarity is greater than or equal to the preset similarity threshold, it is determined that there are similar sentences to the sentence in the duplicate checking text library; if the maximum similarity is less than the preset similarity threshold, it is determined that there are no similar sentences to the sentence in the duplicate checking text library.

[0035] In a possible implementation, the fingerprint vector of each sentence in the speech transcription text and the fingerprint vector of each sentence in the duplicate checking text library are binary vectors of the same length;

[0036] The method of calculating the similarity between the fingerprint vector of the sentence and the fingerprint vectors of each sentence in the duplicate checking text library comprises:

[0037] Calculate the Hamming distance between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library;

[0038] According to the number of elements in the fingerprint vector of the sentence and the calculated Hamming distance, the similarity between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library is determined.

[0039] In a possible implementation, the step of determining the deduction of the answer speech in the text duplication checking dimension according to the judgment results corresponding to each sentence of the speech transcription text includes:

[0040] Determine the number of similar characters in each sentence of the speech transcription text according to the discrimination results corresponding to each sentence of the speech transcription text;

[0041] The deduction score of the answer speech in the text duplication checking dimension is determined based on the number of similar characters in each sentence of the speech transcription text, the number of characters in each sentence of the speech transcription text and the preset maximum deduction score.

[0042] In a possible implementation, determining the number of similar characters in each sentence of the speech transcription text according to the discrimination results corresponding to each sentence of the speech transcription text respectively includes:

[0043] For each sentence of the speech transcription text:

[0044] If there are similar sentences to the sentence in the duplicate checking text library, then the number of similar characters in the sentence is determined to be the number of characters in the sentence;

[0045] If there is no similar sentence to the sentence in the duplicate checking text library, the number of similar characters in the sentence is determined to be 0.

[0046] In a possible implementation, determining the final score of the answering speech according to the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension includes:

[0047] The score of the answering speech in the voice evaluation dimension is weighted according to a preset first weight to obtain a first weighted score, the score of the answering speech in the semantic evaluation dimension is weighted according to a preset second weight to obtain a second weighted score, and the deduction score of the answering speech in the text duplication checking dimension is weighted according to a preset third weight to obtain a weighted deduction score;

[0048] The first weighted score and the second weighted score are summed, and the weighted deduction score is subtracted from the summed score to obtain a final score for the answering speech.

[0049] The second aspect of the present application provides a topic evaluation device, including: a data acquisition module, a speech processing module, a speech evaluation module, a semantic evaluation module, a text duplication detection module and an evaluation result determination module;

[0050] The data acquisition module is used to acquire the topic text and answer voice of the topic;

[0051] The speech processing module is used to extract speech features and transcribe the answering speech to obtain speech features and speech transcribed text of the answering speech;

[0052] The speech evaluation module is used to determine the score of the answer speech in the speech evaluation dimension according to the speech features and the speech transcription text;

[0053] The semantic evaluation module is used to determine the score of the answer speech in the semantic evaluation dimension according to the speech transcription text and the question text;

[0054] The text duplication checking module is used to determine the fingerprint vector of each sentence of the speech transcription text in combination with the position information of each word in the sentence, and to determine whether there is a similar sentence of the sentence in the duplication checking text library by calculating the similarity between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplication checking text library, so as to obtain the judgment result corresponding to the sentence, and determine the deduction of the answer speech in the text duplication checking dimension according to the judgment results corresponding to each sentence of the speech transcription text;

[0055] The evaluation result determination module is used to determine the final score of the answering speech according to the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension and the deduction in the text duplication checking dimension.

[0056] A third aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0057] The memory is used to store computer programs;

[0058] The processor is used to execute the computer program so that the electronic device can implement the steps of any one of the above-mentioned topic evaluation methods.

[0059] A fourth aspect of the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the steps of any one of the above-mentioned topic evaluation methods.

[0060] A fifth aspect of the present application provides a computer program product, comprising computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the steps of any one of the above-mentioned methods for evaluating a topic.

[0061] By means of the above technical scheme, the topic speaking evaluation method provided by the present application first obtains the topic question text and the answering voice, then performs voice feature extraction and voice transcription on the answering voice to obtain the voice features and voice transcribed text of the answering voice, then determines the score of the answering voice in the voice evaluation dimension according to the voice features and voice transcribed text of the answering voice, determines the score of the answering voice in the semantic evaluation dimension according to the voice transcribed text of the answering voice and the topic question text, determines the deduction of the answering voice in the text duplication checking dimension by checking for duplicates for each sentence of the voice transcribed text, and finally determines the final score of the answering voice according to the score of the answering voice in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension. In addition to evaluating the answering speech in the voice evaluation dimension and the semantic evaluation dimension, the topic-speaking evaluation method provided in this application also performs a text check on the voice transcription text of the answering speech, and determines the deduction based on the text check. Then, the final score of the answering speech is determined by comprehensively considering the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text check dimension. The topic-speaking evaluation method provided in this application can evaluate the answering speech of the respondent on the topic in a relatively comprehensive manner. The evaluation results obtained by evaluating the answering speech using the topic-speaking evaluation method provided in this application can objectively reflect the ability of the respondent. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0063] Figure 1 A schematic diagram of a system architecture involved in this application;

[0064] Figure 2 A schematic diagram of a hardware structure of a terminal provided in an embodiment of the present application;

[0065] Figure 3 A schematic diagram of a hardware structure of a server provided in an embodiment of the present application;

[0066] Figure 4 A flowchart of a method for evaluating a topic provided in an embodiment of the present application;

[0067] Figure 5 A sentence S of a speech transcription text provided in an embodiment of the present application i A schematic diagram of the process of determining the position feature vector of each word obtained by performing word segmentation processing;

[0068] Figure 6 A sentence S of the speech transcription text provided in the embodiment of the present application i A schematic diagram of the process of determining the fingerprint vector;

[0069] Figure 7 A schematic diagram of the structure of a topic evaluation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0071] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0072] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0073] In one possible implementation, Figure 1 As shown, the system architecture involved in the present application may include a terminal 101 and a server 102, and the terminal 101 may interact with the server 102 via a network (wired network or wireless network). The server 102 may include one or more servers ( Figure 1 The example includes a server. The terminal 101 can obtain the topic text and the answer voice of the topic, and transmit the topic text and the answer voice of the topic to the server 102 through the network. The server 102 uses the topic evaluation method provided in the present application to evaluate the answer voice of the topic, and feeds back the final evaluation result to the terminal.

[0074] In another possible implementation, the system architecture involved in the present application may include a terminal. The terminal has strong data processing capabilities. The terminal can obtain the topic text and the answer voice of the topic, and use the topic evaluation method provided in the present application to evaluate the answer voice of the topic.

[0075] Next, the product form of the above-mentioned terminal is described.

[0076] The above-mentioned terminal can be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, a robot, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc., and the embodiments of the present application do not impose any restrictions on this.

[0077] Figure 2 An optional hardware structure diagram of a terminal is shown.

[0078] refer to Figure 2 As shown, the terminal may include a radio frequency unit 210, a memory 220, an input unit 230, a display unit 240, a camera 250 (optional), an audio circuit 260 (optional), a speaker 261 (optional), a microphone 262 (optional), an earphone jack 263 (optional), a processor 270, an external interface 280, a power supply 290 and other components. Those skilled in the art will appreciate that Figure 2 This is merely an example of a terminal and does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0079] The input unit 230 can be used to receive input digital or character information, and generate key signal input related to the user settings and function control of the terminal. Specifically, the input unit 230 may include a touch screen 231 (optional) and / or other input devices 232. The touch screen 231 can collect the user's touch operations on or near it (such as the user's operation on or near the touch screen using any suitable object such as fingers, joints, stylus, etc.), and drive the corresponding connection device according to a pre-set program. The touch screen can detect the user's touch action on the touch screen, convert the touch action into a touch signal and send it to the processor 270, and can receive and execute the command sent by the processor 270; the touch signal at least includes the contact point coordinate information. The touch screen 231 can provide an input interface and an output interface between the terminal and the user. In addition, the touch screen can be implemented using multiple types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch screen 231, the input unit 230 can also include other input devices. Specifically, other input devices 232 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

[0080] The display unit 240 may be used to display information input by a user or information provided to a user, various menus of the terminal, an interactive interface, file display, and / or playback of any multimedia file.

[0081] The memory 220 can be used to store instructions and data. The memory 220 can mainly include an instruction storage area and a data storage area. The data storage area can store various data, such as multimedia files, texts, etc. The instruction storage area can store software units such as operating systems, applications, and instructions required for at least one function, or their subsets and extensions. It can also include a non-volatile random access memory; it provides the processor 270 with hardware, software, and data resources including management of computing and processing equipment, and supports control software and applications. It is also used for the storage of multimedia files, and the storage of running programs and applications.

[0082] The processor 270 is the control center of the terminal. It uses various interfaces and lines to connect various parts of the entire terminal. By running or executing instructions stored in the memory 220 and calling data stored in the memory 220, it executes various functions of the terminal and processes data, thereby controlling the terminal as a whole. Optionally, the processor 270 may include one or more processing units; preferably, the processor 270 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application program, and the modem processor mainly processes wireless communication. It is understandable that the above-mentioned modem processor may not be integrated into the processor 270. In some embodiments, the processor and the memory may be implemented on a single chip, and in some embodiments, they may also be implemented separately on separate chips. The processor 270 may also be used to generate corresponding operation control signals, send them to corresponding components of the computing and processing device, read and process data in the software, especially read and process data and programs in the memory 220, so that each functional module therein performs corresponding functions, thereby controlling the corresponding components to act according to the requirements of the instructions.

[0083] Among them, the memory 220 can be used to store software codes related to the topic evaluation method, the processor 270 can execute the software codes in the memory 220, and can also schedule other units (such as the above-mentioned input unit 230 and display unit 240) to realize corresponding functions.

[0084] The radio frequency unit 210 (optional) can be used for receiving and sending information or receiving and sending signals during a call, for example, after receiving the downlink information of the base station, it is sent to the processor 270 for processing; in addition, the designed uplink data is sent to the base station. Generally, the radio frequency unit 210 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LowNoiseAmplifier, LNA), a duplexer, etc. In addition, the radio frequency unit 210 can also communicate with network devices and other devices through wireless communication. The wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile communication (Global System of Mobile communication, GSM), General Packet Radio Service (General PacketRadio Service, GPRS), Code Division Multiple Access (Code Division Multiple Access, CDMA), Wideband Code Division Multiple Access (Wideband Code Division Multiple Access, WCDMA), Long Term Evolution (Long Term Evolution, LTE), email, Short Messaging Service (SMS), etc.

[0085] In the embodiment of the present application, the radio frequency unit 210 can send data to other devices, and can also receive data sent by other devices. It should be understood that the radio frequency unit 210 is optional, and can be replaced by other communication interfaces, such as a network port.

[0086] The terminal also includes a power supply 290 (such as a battery) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 270 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions.

[0087] The terminal also includes an external interface 280, which may be a standard Micro USB interface or a multi-pin connector, and may be used to connect the terminal to communicate with other devices, or to connect a charger to charge the terminal.

[0088] Although not shown, the terminal may further include a flashlight, a wireless fidelity (WiFi) module, a Bluetooth module, sensors with different functions, etc., which will not be described in detail here.

[0089] Next, the product form of the above server is described.

[0090] Figure 3 A structural diagram of the above server is provided, such as Figure 3 As shown, the server may include a bus 301, a processor 302, a communication interface 303, and a memory 304. The processor 302, the memory 304, and the communication interface 303 communicate with each other via the bus 301.

[0091] The bus 301 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0092] The processor 302 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0093] The memory 304 may include a volatile memory, such as a random access memory (RAM). The memory 304 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0094] The memory 304 can be used to store software codes related to the topic evaluation method. The processor 302 can call the software codes stored in the memory 304, and can also schedule other units to implement corresponding functions.

[0095] The processors in the above-mentioned terminals and servers (for example, processor 270 and processor 302) may be hardware circuits (such as application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), general-purpose processors, digital signal processors (DSP), microprocessors or microcontrollers, etc.), or combinations of these hardware circuits. For example, the processor may be a hardware system with an instruction execution function, such as a CPU, DSP, etc., or a hardware system without an instruction execution function, such as an ASIC, FPGA, etc., or a combination of the above-mentioned hardware systems without an instruction execution function and hardware systems with an instruction execution function.

[0096] The inventor of this case discovered in the process of implementing this case that in some application scenarios (such as Mandarin test scenarios), the question bank for speaking topics is fixed, and the fixed question bank for speaking topics may lead to the situation where the respondent directly recites the model essay. If this happens, the evaluation results obtained by using the current speaking topic evaluation scheme to evaluate the answer voice cannot objectively reflect the ability of the respondent.

[0097] In view of the defects of the current speaking topic evaluation scheme, the inventor of this case conducted research and, through continuous research, finally proposed a speaking topic evaluation method with better effect. By evaluating the answering voice through this speaking topic evaluation method, an evaluation result that can objectively reflect the ability of the respondent can be obtained.

[0098] Next, the topic evaluation method provided by this application is introduced through the following embodiments.

[0099] See also Figure 4 , shows a flow chart of a method for evaluating a topic of speech provided by an embodiment of the present application, and the method for evaluating a topic of speech may include:

[0100] Step S401: Obtain the topic text and answer voice.

[0101] The answering voice is the answering voice of the respondent to the topic.

[0102] Step S402: extracting speech features and transcribing the answering speech to obtain speech features and speech transcription text of the answering speech.

[0103] In a possible implementation, the process of extracting speech features and transcribing the answering speech to obtain the speech features and the speech transcription text of the answering speech includes:

[0104] Step S4021a: extract speech features of the answering speech to obtain speech features of the answering speech.

[0105] The speech features extracted from the answering speech may be, but are not limited to, MFCC features, Fbank features, and the like.

[0106] Step S4021b: extract the voiced speech segment from the answering speech by performing speech endpoint detection on the answering speech.

[0107] By extracting voiced speech segments through speech endpoint detection, long speech can be segmented to obtain sentence-level speech information. Speech endpoint detection, also known as voice activity detection (VAD), refers to accurately determining the starting and ending points of a speech from a signal containing speech.

[0108] In order to improve data processing efficiency, step S4021a and step S4021b may be executed in parallel. Of course, this embodiment is not limited thereto, and step S4021a and step S4021b may also be executed in series, for example, step S4021a is executed first, and then step S4021b is executed.

[0109] Step S4022: Acquire the speech features of the voiced speech segment from the speech features of the answering speech, and perform speech transcription according to the speech features of the voiced speech segment to obtain the speech transcription text of the answering speech.

[0110] After obtaining the speech features of the answering speech and the voiced speech segments in the answering speech, the speech features of the voiced speech segments can be extracted from the speech features of the answering speech, and then the speech features of the voiced speech segments can be input into the speech transcription module for speech transcription to obtain speech transcription text.

[0111] In addition to obtaining the speech features and speech transcribed text of the answering speech using the method of steps S4021 to S4022, other methods may be used to obtain the speech features and speech transcribed text of the answering speech. For example, speech features of the answering speech may be extracted to obtain the speech features of the answering speech, and speech transcription may be performed based on the speech features of the answering speech to obtain the speech transcribed text of the answering speech.

[0112] Step S403a: Determine the score of the answering speech in the speech evaluation dimension according to the speech features of the answering speech and the speech transcription text.

[0113] In one possible implementation, the process of determining the score of the answering speech in the speech evaluation dimension based on the speech features of the answering speech and the speech transcribed text may include: converting the speech transcribed text into a phoneme sequence and a tone sequence; inputting the speech features of the answering speech and the phoneme sequence and tone sequence obtained by converting the speech transcribed text into a speech evaluation model for evaluation, and obtaining the score output by the speech evaluation model, which is the score output by the speech evaluation model.

[0114] The speech evaluation model may be, but is not limited to, an encoder-decoder based Transformer model.

[0115] Step S403b: Determine the score of the answering speech in the semantic evaluation dimension according to the speech transcription text of the answering speech and the title text of the topic.

[0116] In one possible implementation, the process of determining the score of the answering speech in the semantic evaluation dimension based on the speech-transcribed text of the answering speech and the title text of the topic may include: concatenating the speech-transcribed text of the answering speech with the title text of the topic to obtain a concatenated text; mapping each word of the concatenated text to an integer id through a sub-word dictionary to obtain a text vector corresponding to the concatenated text; inputting the text vector corresponding to the concatenated text into a semantic evaluation model for evaluation to obtain a score output by the semantic evaluation model, and the score output by the semantic evaluation model is the score of the answering speech in the semantic evaluation dimension.

[0117] The semantic evaluation model may be, but is not limited to, an encoder-based Transformer model. Optionally, the encoder-based Transformer model may include an embedding layer, a multi-layer Transformer based on a self-attention mechanism, and a fully connected layer. Assume that the text vector corresponding to the concatenated text is a text vector x=(x1,…,x U ), where U represents the length of the concatenated text and V represents the size of the subword dictionary. The text vector x corresponding to the concatenated text is x=(x1,…,x U) is input into the encoder-based Transformer model and then processed by the embedding layer to obtain a hidden vector h=(h1,…,h U ), the hidden vector h of dimension (U, d) is input into the multi-layer Transformer based on the self-attention mechanism to obtain the hidden vector c=(c1,…,c U ), take c1 of the hidden layer vector c and process it through the fully connected layer to obtain the score of the answer speech in the semantic evaluation dimension.

[0118] Step S403c-1: For each sentence in the speech transcription text, the fingerprint vector of the sentence is determined in combination with the position information of each word in the sentence, and the similarity between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library is calculated to determine whether there are similar sentences to the sentence in the duplicate checking text library, so as to obtain the judgment result corresponding to the sentence.

[0119] Considering that in some application scenarios (such as Mandarin test scenarios), the question bank for speaking topics is fixed, and the fixed question bank for speaking topics may lead to the situation where the respondent directly recites the model essay. In view of this situation, this embodiment proposes to perform text duplication checking on the speech transcribed text.

[0120] Specifically, for each sentence in the speech transcription text, the fingerprint vector of the sentence is determined, and the similarity between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library is calculated to determine whether there are similar sentences to the sentence in the duplicate checking text library, so as to obtain the judgment result corresponding to the sentence.

[0121] In order to reduce the misjudgment phenomenon caused by the different positions of words in two sentences, this embodiment determines the fingerprint vector of each sentence in the speech transcription text in combination with the position information of the words in the sentence.

[0122] Step S403c-2: Determine the deduction points for the answer voice in the text duplication check dimension according to the judgment results corresponding to each sentence of the voice transcription text.

[0123] Among them, the discrimination result corresponding to any sentence in the speech transcription text is used to indicate whether there is a similar sentence to the sentence in the duplicate checking text library. After obtaining the discrimination results corresponding to each sentence of the speech transcription text, the deduction of the answer speech in the text duplicate checking dimension can be determined according to the discrimination results corresponding to each sentence of the speech transcription text.

[0124] Step S404: Determine the final score of the answering speech according to the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension.

[0125] After obtaining the score of the answering voice in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension, the scores of the three dimensions are combined to get the final score of the answering voice.

[0126] The topic-speaking evaluation method provided in the embodiment of the present application first obtains the topic-speaking title text and the answering speech, then performs speech feature extraction and speech transcription on the answering speech to obtain the speech features and speech transcription text of the answering speech, then determines the score of the answering speech in the speech evaluation dimension according to the speech features and speech transcription text of the answering speech, determines the score of the answering speech in the semantic evaluation dimension according to the speech transcription text of the answering speech and the topic-speaking title text, determines the deduction of the answering speech in the text duplication checking dimension by checking for plagiarism for each sentence of the speech transcription text, and finally determines the final score of the answering speech according to the score of the answering speech in the speech evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension. The topic-speaking evaluation method provided in the embodiment of the present application not only evaluates the answering speech in the voice evaluation dimension and the semantic evaluation dimension, but also performs a text duplication check on the voice transcription text of the answering speech, and determines the deduction points based on the text duplication check situation, and then determines the final score of the answering speech by comprehensively considering the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction points in the text duplication check dimension. The topic-speaking evaluation method provided in the embodiment of the present application can evaluate the answering speech of the respondent on the topic in a relatively comprehensive manner, and the evaluation results obtained by evaluating the answering speech through the topic-speaking evaluation method provided in the embodiment of the present application can objectively reflect the ability of the respondent.

[0127] In another embodiment of the present application, the specific implementation process of "Step S403c-1: for each sentence of the speech transcription text, determine the fingerprint vector of the sentence in combination with the position information of each word in the sentence, and calculate the similarity between the fingerprint vector of the sentence and the fingerprint vectors of each sentence in the duplicate checking text library, to determine whether there are similar sentences to the sentence in the duplicate checking text library, so as to obtain the determination result corresponding to the sentence" in the above embodiment is introduced.

[0128] First, determine a sentence S of the speech transcription text i The process of obtaining the fingerprint vector is introduced in the following section.

[0129] Determine a sentence S of the speech transcription text i The fingerprint vector process can include:

[0130] Step a1: Sentence S i Perform word segmentation to obtain several words.

[0131] Existing word segmentation algorithms (such as Jieba word segmentation algorithm) can be used to segment sentence S iPerform word segmentation processing.

[0132] Step a2: For each word obtained through word segmentation, a hash value of the word is determined using a hash function, and the hash value of the word is calculated based on the word in the sentence S. i The position feature vector of the word is determined according to the position in the word, and the target feature vector of the word is determined according to the hash value of the word and the position feature vector of the word.

[0133] For each word obtained by the word segmentation process, the hash value of each word is determined using the same hash function. It should be noted that the hash value of any word is a binary vector.

[0134] In a possible implementation, for a word c obtained through word segmentation processing ij (For sentence S i The jth word obtained by word segmentation) is obtained according to word c ij In sentence S i The position determining word c in ij The process of finding the position feature vector may include:

[0135] Step a21: According to word c ij In sentence S i The position in generates a binary vector v of length L1 ij .

[0136] Among them, L1 is the sentence S i The total number of words contained, binary vector v ij For word c ij The original position feature vector, binary vector v ij The element at each position in can indicate whether there is word c at the corresponding position in the sentence ij .

[0137] For example, for sentence S i After word segmentation, 5 words are obtained. Word c ij It is the sentence S i The third word in , then according to word c ij In sentence S i The position in generates the binary vector {0,0,1,0,0}.

[0138] Step a22: Use the mapping function to transform the binary vector v ij Mapped to a vector of length L2, the mapped vector u is obtained ij .

[0139] Wherein, L2 is an integer smaller than L1.

[0140] The mapping function in this embodiment may be a dimensionality reduction function, which can map a vector of a certain length to a vector of a smaller length. For example, the mapping function can map a vector of a length of 8 to a vector of a length of 4. It should be noted that the length of the mapped vector can be determined according to actual conditions. ij The length of the word c ij The hash value h ij (binary vectors) have the same length.

[0141] Step a23, calculate the mapped vector u ij The mean of each element in and the mapped vector u ij Subtract the mean from each element in to get the mean difference vector.

[0142] For example, the mapped vector u ij is {1,2,3,2}, u ij The mean of each element in is 2, and u ij Subtract the mean 2 from each element in to get the mean difference vector {-1,0,1,0}.

[0143] Step a24: Set the elements greater than 0 in the mean difference vector to 1, and the elements less than or equal to 0 to 0, to obtain word c ij The position feature vector p ij .

[0144] After obtaining the mean difference vector, the mean difference vector can be reduced in dimension and the vector after dimension reduction is used as the word c ij The final position feature vector p ij Specifically, the elements greater than 0 in the mean difference vector are set to 1, and the elements less than or equal to 0 are set to 0, to obtain a binary vector, i.e., word c ij The position feature vector p ij .

[0145] In getting the word c ij The hash value h ij (binary vector) and position feature p ij After the vector is obtained, the word c ij The hash value h ij and word c ij The position feature vector p ij Determine word c ij The target feature vector.

[0146] According to the word c ij The hash value h ij and word c ij The position feature vector determines the word c ij There are many ways to implement the target feature vector of . In one possible implementation, the word c can be directly ijThe hash value h ij With the word c ij The position feature vectors are fused to get word c ij The fused vector f ij , the word c ij The fused vector f ij As word c ij Optionally, a weighted summation fusion method can be used to combine word c ij The hash value h ij With the word c ij The position feature vector p ij Fusion, as shown below:

[0147] f ij =μ×h ij +(1-μ)×p ij (1)

[0148] It should be noted that when using μ to h ij When weighted, for h ij For each element in , if the element is 1, multiply the element by μ, if the element is 0, subtract μ from the element. Similarly, when using 1-μ for p ij When weighted, for p ij For each element in , if the element is 1, multiply the element by 1-μ; if the element is 0, subtract 1-μ from the element.

[0149] It should also be noted that this embodiment is not limited to using the weighted sum fusion method to combine the word c ij The hash value h ij With the word c ij The position feature vector p ij Fusion, other fusion methods can also be used to combine word c ij The hash value h ij With the word c ij The position feature vector p ij Fusion, for example, can adopt the fusion method of direct summation and so on.

[0150] Considering that some words (such as "yes", "no", "today", etc.) are common in many types of texts, such words can be ignored in the process of checking for duplicates, while some words only appear in specific situations and have a greater impact on checking for duplicates, this embodiment provides a method for checking for duplicates based on word c ij The hash value h ij and word c ij The position feature vector p ij Determine word c ij The second implementation of the target feature vector is:

[0151] The word cij The hash value h ij With the word c ij The position feature vector p ij Fusion, get word c ij The fused vector f ij ; According to the word c ij In sentence S i The number of occurrences in the sentence S i The total number of words included, the total number of words in the duplicate check text library, the total number of words in the duplicate check text library ij The number of occurrences, determine the word c ij The corresponding weight w ij ; Word c ij The corresponding weight w ij Word c ij The fused vector f ij Weighted, get word c ij The target feature vector.

[0152] In one possible implementation, the word c ij The corresponding weight w ij It can be calculated by the following formula:

[0153] (2)

[0154] Among them, m ij Representation word c ij In sentence S i The number of occurrences in M i Represents sentence S i The total number of words contained, N represents the total number of words in the duplicate check text library, n ij Represents word c in the duplicate check text library ij Number of occurrences.

[0155] The word c can be determined as above ij The target feature vector is used to determine the sentence S i The target feature vector of each other word obtained by word segmentation.

[0156] Step a3: According to the sentence S i The target feature vector of each word obtained by word segmentation is used to determine the sentence S i Fingerprint vector.

[0157] In a possible implementation, according to the sentence S i The target feature vector of each word obtained by word segmentation is used to determine the sentence S i The fingerprint vector process may include: first, the sentence S iSum the target feature vectors of each word obtained by word segmentation to obtain the summed vector Z i , then sum the vector Z i Perform dimensionality reduction processing. Specifically, the summed vector Z i Set the elements greater than 0 to 1, and set the elements less than or equal to 0 to 0. The vector after dimensionality reduction is used as the sentence S i Fingerprint vector.

[0158] See also Figure 5 , showing that by i Schematic diagram of the process of determining the positional feature vectors of each word obtained through word segmentation. For each word obtained through word segmentation, the final positional feature vector of the word can be obtained through a series of processes such as original positional feature vector generation, position mapping, mean subtraction, and dimensionality reduction. Figure 6 , showing that sentence S i The process of determining the fingerprint vector of the word segmentation process is as follows: for each word obtained by the word segmentation process, the target feature vector of the word can be obtained through a series of processes such as determining the hash value, determining the position feature vector, fusing the hash value and the position feature vector, and weighting the fusion vector. After obtaining the target feature vectors of each word obtained by the word segmentation process, the target feature vectors of each word are summed and reduced in dimension to obtain the sentence S. i Fingerprint vector.

[0159] In getting sentence S i After obtaining the fingerprint vector, we can convert the sentence S i The fingerprint vector of S is calculated with the fingerprint vector of each sentence in the duplicate text library to determine whether the sentence S exists in the duplicate text library. i , to get sentence S i The corresponding identification results.

[0160] Specifically, by replacing the sentence S i The fingerprint vector of S is calculated with the fingerprint vector of each text in the duplicate checking text library to determine whether there is sentence S in the duplicate checking text library. i The process of similar sentences can include:

[0161] Step b1: Sentence S i The fingerprint vector of the sentence is calculated with the fingerprint vector of each sentence in the duplicate text library to obtain several similarities.

[0162] For example, the duplicate checking text library includes 100 sentences. i The fingerprint vector of the sentence is calculated similarly with the fingerprint vector of each sentence in the duplicate text library, and 100 similarities can be obtained.

[0163] The method of determining the fingerprint vector of each sentence in the duplicate text library is similar to sentence S i The method of determining the fingerprint vector is similar to that of the method of determining the fingerprint vector. In order to improve the evaluation efficiency, the fingerprint vector of each sentence in the duplicate checking text library can be determined in advance (that is, the fingerprint vector of each sentence in the duplicate checking text library is determined before the evaluation). In this way, in the evaluation stage, the predetermined fingerprint vector can be directly used. Of course, this embodiment is not limited to this. For example, the fingerprint vector of the sentence in the duplicate checking text library can also be determined in real time in the evaluation stage.

[0164] In one possible implementation, the sentence S i The process of calculating the similarity between the fingerprint vector of the sentence S and the fingerprint vector of each sentence in the duplicate checking text library may include: calculating the sentence S i The Hamming distance between the fingerprint vector of the sentence S and the fingerprint vector of each sentence in the duplicate checking text library; i The number of elements in the fingerprint vector and the calculated Hamming distance determine the sentence S i The similarity between the fingerprint vector of and the fingerprint vector of each sentence in the duplicate checking text library.

[0165] For each sentence in the duplicate text library, calculate sentence S i The Hamming distance between the fingerprint vector of the sentence in the duplicate checking text library and the fingerprint vector of the sentence S i The number of elements in the fingerprint vector determines the sentence S i The similarity between the fingerprint vector of the sentence and the fingerprint vector of the sentence in the duplicate checking text library.

[0166] Specifically, sentence S i The fingerprint vector of a sentence Sd in the duplicate text library j The similarity f(S i ,Sd j ) can be calculated as follows:

[0167] (3)

[0168] Among them, the sentence S i The fingerprint vector and the sentence Sd in the duplicate text library j The fingerprint vectors of are all binary vectors with the same length. num represents the sentence S i The length of the fingerprint vector or sentence Sd j The length of the fingerprint vector, Ham(S i ,Sd j ) represents sentence S i The fingerprint vector and the sentence Sd in the duplicate text library j The Hamming distance of the fingerprint vector of the sentence S iThe fingerprint vector and sentence Sd j The number of different elements in the fingerprint vector, for example, sentence S i The fingerprint vector and sentence Sd j The fingerprint vector of Ham(S i ,Sd j ) is 2, Ham(S i ,Sd j ) is smaller, f(S i ,Sd j ) is larger, the sentence S i With the sentence Sd j The more similar.

[0169] Step b2: Obtain the maximum similarity from several similarities.

[0170] Obtain the maximum similarity f from the several similarities obtained in step b1 maxi .

[0171] Step b3-a: If the maximum similarity is greater than or equal to the preset similarity threshold, it is determined that there is sentence S in the duplicate checking text library. i Similar sentences of .

[0172] If the maximum similarity f maxi If the similarity is greater than or equal to the preset similarity threshold δ, it means that there is sentence S in the duplicate text library. i Similar sentences, fingerprint vector and sentence S i The similarity of the fingerprint vector is the maximum similarity f maxi The sentence S is i Similar sentences of .

[0173] Step b3-b: If the maximum similarity is less than a preset similarity threshold, it is determined that there is no similar sentence to the sentence in the duplicate checking text library.

[0174] If the maximum similarity f maxi If the similarity is less than the preset threshold δ, it means that the sentence S does not exist in the duplicate text library. i Similar sentences of .

[0175] Through the above steps b1 to b3, sentence S can be obtained i The corresponding judgment result is used to indicate whether sentence S exists in the duplicate text library. i Similar sentences of .

[0176] By adopting the method of steps b1 to b3, the discrimination results corresponding to other sentences of the speech transcription text can be obtained, so that the discrimination results corresponding to each sentence of the speech transcription text can be obtained.

[0177] In another embodiment of the present application, the specific implementation process of "Step S403c-2: determining the deduction of the answer voice in the text duplication checking dimension according to the judgment results corresponding to each sentence of the speech transcription text" in the above embodiment is introduced.

[0178] In a possible implementation, the specific implementation process of determining the deduction of the answer voice in the text duplication check dimension according to the judgment results corresponding to each sentence of the voice transcription text may include:

[0179] Step c1: Determine the number of similar characters in each sentence of the speech transcription text according to the discrimination results corresponding to each sentence of the speech transcription text.

[0180] In one possible implementation, the process of determining the number of similar characters in each sentence of the speech-transcribed text according to the discrimination results corresponding to each sentence of the speech-transcribed text may include: for each sentence of the speech-transcribed text, if there is a similar sentence to the sentence in the duplicate checking text library, then determining the number of similar characters in the sentence to be the total number of characters in the sentence; if there is no similar sentence to the sentence in the duplicate checking text library, then determining the number of similar characters in the sentence to be 0.

[0181] Step c2: determine the deduction score for the answer voice in the text duplication checking dimension based on the number of similar characters in each sentence of the speech transcription text, the number of characters in each sentence of the speech transcription text, and the preset maximum deduction score.

[0182] The deduction of answer voice in the text duplication check dimension can be calculated as follows:

[0183] (4)

[0184] Among them, N i represents the number of similar words in the i-th sentence of the speech transcription text, M i represents the total number of words in the i-th sentence of the speech transcription text, and n represents the number of sentences contained in the speech transcription text. If there are similar sentences to the i-th sentence of the speech transcription text in the text duplicate checking database, then N i =M i , if there is no similar sentence to the i-th sentence of the speech transcription text in the text duplication check database, then N i =0,score max_similarity_deduction Indicates the preset maximum deduction score, score imilarity_deduction Indicates the points deducted for answering voice in the text duplication check dimension.

[0185] Through the above process, the deduction points of the answer voice in the text duplication check dimension can be obtained.

[0186] In another embodiment of the present application, the specific implementation process of "Step S404: determining the final score of the answering speech according to the score of the answering speech in the speech evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension" in the above embodiment is introduced.

[0187] In one possible implementation, the process of determining the final score of the answering speech based on the score of the answering speech in the speech evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension may include: weighting the score of the answering speech in the speech evaluation dimension according to a preset first weight α to obtain a first weighted score, weighting the score of the answering speech in the semantic evaluation dimension according to a preset second weight β to obtain a second weighted score, and weighting the deduction in the text duplication checking dimension of the answering speech according to a preset third weight γ to obtain a weighted deduction; summing the first weighted score and the second weighted score, and subtracting the weighted deduction from the summed score to obtain the final score of the answering speech.

[0188] Final score of the answer voice total The determination method is as follows:

[0189] score total =α×score speech +β×score semantic -γ×score imilarity_deduction (5)

[0190] Among them, score speech Indicates the score of the answering voice in the voice evaluation dimension, score semantic Indicates the score of the answer speech in the semantic evaluation dimension, score imilarity_deduction It indicates the deduction of answer voice in the text duplication check dimension. The values ​​of the three weights α, β, and γ can be determined according to the actual application scenario.

[0191] The above introduces the topic evaluation method provided in the embodiment of the present application, and the device corresponding to the above topic evaluation method will be introduced below.

[0192] See also Figure 7 , Figure 7 A structural schematic diagram of a speaking topic evaluation device provided in an embodiment of the present application, the speaking topic evaluation device may include: a data acquisition module 701, a speech processing module 702, a speech evaluation module 703a, a semantic evaluation module 703b, a text duplication checking module 703c and an evaluation result determination module 704.

[0193] The data acquisition module 701 is used to acquire the topic text and answer voice of the topic.

[0194] The speech processing module 702 is used to extract speech features and transcribe the answering speech to obtain speech features and speech transcribed text of the answering speech.

[0195] The speech evaluation module 703a is used to determine the score of the answering speech in the speech evaluation dimension according to the speech features and speech transcription text of the answering speech.

[0196] The semantic evaluation module 703b is used to determine the score of the answering speech in the semantic evaluation dimension according to the speech transcription text of the answering speech and the title text of the topic.

[0197] The text duplication checking module 703c is used to determine the fingerprint vector of each sentence in the speech transcription text in combination with the position information of each word in the sentence, and to determine whether there are similar sentences to the sentence in the duplication checking text library by calculating the similarity between the fingerprint vector of the sentence and the fingerprint vectors of each sentence in the duplication checking text library, so as to obtain the judgment result corresponding to the sentence, and determine the deduction of the answer speech in the text duplication checking dimension according to the judgment results corresponding to each sentence in the speech transcription text.

[0198] The evaluation result determination module 704 is used to determine the final score of the answering speech according to the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension and the deduction score in the text duplication checking dimension.

[0199] In a possible implementation, the speech processing module 702 may include: a speech feature extraction module, a voiced speech segment extraction module, and a speech transcription text acquisition module.

[0200] The speech feature extraction module is used to extract the speech features of the answering speech to obtain the speech features of the answering speech.

[0201] The voiced speech segment extraction module is used to extract the voiced speech segment from the answering speech by performing speech endpoint detection on the answering speech.

[0202] The speech transcription text acquisition module is used to obtain the speech features of the voiced speech segment from the speech features of the answering speech, and perform speech transcription according to the speech features of the voiced speech segment to obtain the speech transcription text of the answering speech.

[0203] In a possible implementation, the text duplication checking module 703c determines the fingerprint vector of the sentence in combination with the position information of each word in the sentence, including:

[0204] Perform word segmentation on the sentence to obtain several words;

[0205] For each of the plurality of words, a hash value of the word is determined by using a hash function, and a position feature vector of the word is determined according to the position of the word in the sentence, and a target feature vector of the word is determined according to the hash value of the word and the position feature vector of the word;

[0206] According to the target feature vectors of several words, the fingerprint vector of the sentence is determined.

[0207] In a possible implementation, when determining the position feature vector of the word according to the position of the word in the sentence, the text duplication checking module 703c is specifically configured to:

[0208] Generate a binary vector of length L1 according to the position of the word in the sentence, where L1 is the total number of words in the sentence, and the element at each position in the binary vector can indicate whether the word exists at the corresponding position in the sentence;

[0209] Using a mapping function, the binary vector is mapped into a vector of length L2 to obtain a mapped vector, where L2 is an integer less than L1;

[0210] Calculate the mean of each element in the mapped vector, and subtract the mean from each element in the mapped vector to obtain a mean difference vector;

[0211] Set the elements in the mean difference vector that are greater than 0 to 1, and the elements that are less than or equal to 0 to 0, and obtain the position feature vector of the word.

[0212] In a possible implementation, when determining the target feature vector of the word according to the hash value of the word and the position feature vector of the word, the text duplication checking module 703c is specifically used to:

[0213] The hash value of the word is fused with the position feature vector of the word to obtain the fused vector of the word;

[0214] Determine the weight corresponding to the word according to the number of times the word appears in the sentence, the total number of words contained in the sentence, the total number of words in the duplicate check text library, and the number of times the word appears in the duplicate check text library;

[0215] The fused vector of the word is weighted by the weight corresponding to the word to obtain the target feature vector of the word.

[0216] In a possible implementation, when determining the fingerprint vector of the sentence according to the target feature vectors of several words, the text duplication checking module 703c is specifically used to:

[0217] Fuse the target feature vectors of several words to obtain a fused vector;

[0218] Set the elements greater than 0 in the fused vector to 1, and the elements less than or equal to 0 to 0, to obtain the fingerprint vector of the sentence.

[0219] In a possible implementation, the text duplication checking module 703c is specifically used to:

[0220] The fingerprint vector of the sentence is respectively calculated with the fingerprint vectors of each sentence in the duplicate checking text library to obtain several similarities;

[0221] Obtain the maximum similarity from several similarities;

[0222] If the maximum similarity is greater than or equal to the preset similarity threshold, it is determined that there are similar sentences to the sentence in the duplicate checking text library. If the maximum similarity is less than the preset similarity threshold, it is determined that there are no similar sentences to the sentence in the duplicate checking text library.

[0223] In a possible implementation, the fingerprint vector of each sentence in the speech transcription text and the fingerprint vector of each sentence in the duplicate checking text library are binary vectors of the same length.

[0224] When the text duplicate checking module 703c respectively calculates the similarity between the fingerprint vector of the sentence and the fingerprint vectors of each sentence in the duplicate checking text library, it is specifically used to:

[0225] Calculate the Hamming distance between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library;

[0226] According to the number of elements in the fingerprint vector of the sentence and the calculated Hamming distance, the similarity between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library is determined.

[0227] In a possible implementation, the text duplication checking module 703c is specifically used to determine the deduction of the answer voice in the text duplication checking dimension according to the judgment results corresponding to each sentence of the voice transcription text:

[0228] Determine the number of similar characters in each sentence of the speech transcription text according to the discrimination results corresponding to each sentence of the speech transcription text;

[0229] The deduction score for the answer voice in the text duplication checking dimension is determined based on the number of similar characters in each sentence of the speech-transcribed text, the number of characters in each sentence of the speech-transcribed text, and the preset maximum deduction score.

[0230] In a possible implementation, the text duplication checking module 703c is specifically used to determine the number of similar characters in each sentence of the speech transcription text according to the discrimination results corresponding to each sentence of the speech transcription text:

[0231] For each sentence of the speech transcription text:

[0232] If there are similar sentences to the sentence in the duplicate checking text library, the number of similar characters in the sentence is determined to be the number of characters in the sentence;

[0233] If there is no similar sentence to the sentence in the duplicate checking text library, the number of similar characters in the sentence is determined to be 0.

[0234] In a possible implementation, the evaluation result determination module 704 is specifically used to determine the final score of the answering speech according to the score of the answering speech in the speech evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension:

[0235] The score of the answering voice in the voice evaluation dimension is weighted according to a preset first weight to obtain a first weighted score, the score of the answering voice in the semantic evaluation dimension is weighted according to a preset second weight to obtain a second weighted score, and the deduction score of the answering voice in the text duplication checking dimension is weighted according to a preset third weight to obtain a weighted deduction score;

[0236] The first weighted score and the second weighted score are summed, and the weighted deduction score is subtracted from the summed score to obtain the final score of the answer speech.

[0237] The speech topic evaluation device provided in the embodiment of the present application not only evaluates the answering speech in the voice evaluation dimension and the semantic evaluation dimension, but also checks the text of the speech transcription of the answering speech for duplicate content, and determines the deduction points based on the text duplication check results, and then determines the final score of the answering speech based on the comprehensive score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction points in the text duplication check dimension. The speech topic evaluation device provided in the embodiment of the present application can evaluate the answering speech of the respondent on the speech topic in a relatively comprehensive manner, and the evaluation results obtained by evaluating the answering speech through the speech topic evaluation device provided in the embodiment of the present application can objectively reflect the ability of the respondent.

[0238] An embodiment of the present application also provides an electronic device, which may include: at least one processor, at least one communication interface, at least one memory and at least one communication bus.

[0239] In the embodiment of the present application, the number of the processor, the communication interface, the memory, and the communication bus is at least one, and the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0240] The processor may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application;

[0241] The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

[0242] The memory stores a program, and the processor can call the program stored in the memory, and the program is used to implement the steps of the topic evaluation method provided in the above embodiment.

[0243] An embodiment of the present application also provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the steps of the topic evaluation method provided in the above embodiment.

[0244] An embodiment of the present application also provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the steps of the topic evaluation method provided in the above embodiment.

[0245] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0246] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0247] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0248] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A topic evaluation method, characterized in that: include: Acquire the topic text and answer voice of the topic, and perform voice feature extraction and voice transcription on the answer voice to obtain the voice features and voice transcription text of the answer voice; Determine the score of the answering speech in the speech evaluation dimension according to the speech features and the speech transcription text, and determine the score of the answering speech in the semantic evaluation dimension according to the speech transcription text and the question text; For each sentence of the speech transcription text, the fingerprint vector of the sentence is determined in combination with the position information of each word in the sentence, and the fingerprint vector of the sentence is respectively calculated with the fingerprint vector of each sentence in the duplicate checking text library to determine whether there is a similar sentence to the sentence in the duplicate checking text library, so as to obtain a determination result corresponding to the sentence; Determine the deduction of the answer speech in the text duplication checking dimension according to the judgment results corresponding to each sentence of the speech transcription text; The final score of the answering speech is determined based on the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction points in the text duplication checking dimension.

2. The topic evaluation method according to claim 1, characterized in that: The extracting speech features and transcribing the speech of the answer to obtain the speech features and speech transcription text of the answer speech includes: Extracting speech features of the answering speech to obtain speech features of the answering speech; Extracting a voiced speech segment from the answering speech by performing speech endpoint detection on the answering speech; The speech features of the voiced speech segment are obtained from the speech features of the answering speech, and speech transcription is performed according to the speech features of the voiced speech segment to obtain a speech transcription text of the answering speech.

3. The topic evaluation method according to claim 1, characterized in that: The step of determining the fingerprint vector of the sentence by combining the position information of each word in the sentence includes: Perform word segmentation on the sentence to obtain several words; For each of the plurality of words, a hash value of the word is determined by using a hash function, and a position feature vector of the word is determined according to the position of the word in the sentence, and a target feature vector of the word is determined according to the hash value of the word and the position feature vector of the word; A fingerprint vector of the sentence is determined according to the target feature vectors of the plurality of words.

4. The topic evaluation method according to claim 3 is characterized in that: Determining the position feature vector of the word according to the position of the word in the sentence includes: Generate a binary vector of length L1 according to the position of the word in the sentence, where L1 is the total number of words contained in the sentence, and the element at each position in the binary vector can indicate whether the word exists at the corresponding position in the sentence; Using a mapping function, mapping the binary vector to a vector of length L2 to obtain a mapped vector, wherein L2 is an integer less than L1; Calculate the mean of each element in the mapped vector, and subtract the mean from each element in the mapped vector to obtain a mean difference vector; The elements in the mean difference vector that are greater than 0 are set to 1, and the elements that are less than or equal to 0 are set to 0, to obtain the position feature vector of the word.

5. The topic evaluation method according to claim 3 is characterized in that: The step of determining a target feature vector of the word according to the hash value of the word and the position feature vector of the word includes: The hash value of the word is fused with the position feature vector of the word to obtain the fused vector of the word; Determine the weight corresponding to the word according to the number of times the word appears in the sentence, the total number of words contained in the sentence, the total number of words in the duplicate checking text library, and the number of times the word appears in the duplicate checking text library; The fused vector of the word is weighted by the weight corresponding to the word to obtain the target feature vector of the word.

6. The topic evaluation method according to claim 3 is characterized in that: Determining the fingerprint vector of the sentence according to the target feature vectors of the plurality of words includes: Fusing the target feature vectors of the plurality of words to obtain a fused vector; The elements in the fused vector that are greater than 0 are set to 1, and the elements that are less than or equal to 0 are set to 0, to obtain the fingerprint vector of the sentence.

7. The topic evaluation method according to claim 1, characterized in that: The method of calculating the similarity between the fingerprint vector of the sentence and the fingerprint vectors of each sentence in the duplicate checking text library to determine whether there is a similar sentence to the sentence in the duplicate checking text library comprises: The fingerprint vector of the sentence is respectively calculated with the fingerprint vectors of each sentence in the duplicate checking text library to obtain several similarities; Obtaining a maximum similarity from the plurality of similarities; If the maximum similarity is greater than or equal to the preset similarity threshold, it is determined that there are similar sentences to the sentence in the duplicate checking text library; if the maximum similarity is less than the preset similarity threshold, it is determined that there are no similar sentences to the sentence in the duplicate checking text library.

8. The topic evaluation method according to claim 7, characterized in that: The fingerprint vector of each sentence in the speech transcription text and the fingerprint vector of each sentence in the duplicate checking text library are binary vectors of the same length; The method of calculating the similarity between the fingerprint vector of the sentence and the fingerprint vectors of each sentence in the duplicate checking text library comprises: Calculate the Hamming distance between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library; According to the number of elements in the fingerprint vector of the sentence and the calculated Hamming distance, the similarity between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplicate checking text library is determined.

9. The topic evaluation method according to claim 1, characterized in that: The step of determining the deduction of the answer speech in the text duplication checking dimension according to the judgment results corresponding to each sentence of the speech transcription text includes: Determine the number of similar characters in each sentence of the speech transcription text according to the discrimination results corresponding to each sentence of the speech transcription text; The deduction score of the answer speech in the text duplication checking dimension is determined based on the number of similar characters in each sentence of the speech transcription text, the number of characters in each sentence of the speech transcription text and the preset maximum deduction score.

10. The topic evaluation method according to claim 9, characterized in that: The step of determining the number of similar characters in each sentence of the speech transcription text according to the discrimination results respectively corresponding to each sentence of the speech transcription text comprises: For each sentence of the speech transcription text: If there are similar sentences to the sentence in the duplicate checking text library, then the number of similar characters in the sentence is determined to be the number of characters in the sentence; If there is no similar sentence to the sentence in the duplicate checking text library, the number of similar characters in the sentence is determined to be 0.

11. The topic evaluation method according to claim 1, characterized in that: Determining the final score of the answering speech according to the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension, and the deduction in the text duplication checking dimension includes: The score of the answering speech in the voice evaluation dimension is weighted according to a preset first weight to obtain a first weighted score, the score of the answering speech in the semantic evaluation dimension is weighted according to a preset second weight to obtain a second weighted score, and the deduction score of the answering speech in the text duplication checking dimension is weighted according to a preset third weight to obtain a weighted deduction score; The first weighted score and the second weighted score are summed, and the weighted deduction score is subtracted from the summed score to obtain a final score for the answering speech.

12. A topic evaluation device, characterized in that: include: Data acquisition module, speech processing module, speech evaluation module, semantic evaluation module, text duplication checking module and evaluation result determination module; The data acquisition module is used to acquire the topic text and answer voice of the topic; The speech processing module is used to extract speech features and transcribe the answering speech to obtain speech features and speech transcribed text of the answering speech; The speech evaluation module is used to determine the score of the answer speech in the speech evaluation dimension according to the speech features and the speech transcription text; The semantic evaluation module is used to determine the score of the answer speech in the semantic evaluation dimension according to the speech transcription text and the question text; The text duplication checking module is used to determine the fingerprint vector of each sentence of the speech transcription text in combination with the position information of each word in the sentence, and to determine whether there is a similar sentence of the sentence in the duplication checking text library by calculating the similarity between the fingerprint vector of the sentence and the fingerprint vector of each sentence in the duplication checking text library, so as to obtain the judgment result corresponding to the sentence, and determine the deduction of the answer speech in the text duplication checking dimension according to the judgment results corresponding to each sentence of the speech transcription text; The evaluation result determination module is used to determine the final score of the answering speech according to the score of the answering speech in the voice evaluation dimension, the score in the semantic evaluation dimension and the deduction in the text duplication checking dimension.

13. An electronic device, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the steps of the topic evaluation method as described in any one of claims 1 to 11.

14. A computer storage medium, characterized in that: The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the steps of the topic evaluation method as described in any one of claims 1 to 11.

15. A computer program product, characterized in that It includes computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the steps of the topic evaluation method as described in any one of claims 1 to 11.

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