Voice Recognition Device

Abstract

The purpose of the present invention is to provide a speech recognition device that does not output a recognition result of non-verbal sound. A speech recognition device 100 is provided with: a speech acquisition unit 101 (sound information acquisition unit) that acquires a speech waveform signal (sound information) in a predetermined unit obtained by speech section detection; and a sound information processing unit that performs processing for outputting a result based on speech recognition processing if the speech wavelength signal being the sound information is speech, and not outputting the result based on the speech recognition processing if the sound information is non-verbal. In the present disclosure, the sound information processing unit comprises a speech recognition unit 102, a non-verbal speech recognition unit 103, a score determination unit 104, and a result output unit 105.

Description

[Technical field]

[0001] The present invention relates to a voice recognition device that performs voice recognition. [Background technology]

[0002] Patent Document 1 describes a conference support system that distinguishes between speech segments and non-speech segments contained in voice data during a conference or the like, and recognizes text data indicating speech from the speech segments. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-45208 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above-mentioned speech recognition system has a problem that when non-speech sounds that are difficult to convert into text, such as laughter, nodding, coughing, or keyboard sounds, are input, the system outputs a word text that sounds similar to the input, which is registered in a speech recognition dictionary. Therefore, a sentence that includes such a word text is difficult to read, and it is difficult to use it for minutes of a meeting, etc.

[0005] In order to solve the above-mentioned problems, an object of the present invention is to provide a speech recognition device that does not output recognition results of non-speech sounds. [Means for solving the problem]

[0006] The voice recognition device of the present invention comprises a sound information acquisition unit that acquires sound information in predetermined units, and a sound information processing unit that performs processing to output a result based on voice recognition processing when the sound information is a speech sound, and not to output a result based on the voice recognition processing when the sound information is a non-speech sound. Effect of the Invention

[0007] According to the present invention, it is possible to prevent the output of the recognition result obtained by speech recognition of non-speech sounds. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a functional configuration of a speech recognition device 100 according to the present disclosure. [Diagram 2] 10 is an explanatory diagram showing the reliability of a speech recognition result and the reliability of each recognition event; FIG. [Diagram 3] FIG. 13 is a diagram showing an example of a processing result for an utterance. [Figure 4] FIG. 13 is a diagram showing an outline of a determination process when a determination section is changed. [Diagram 5] 3 is a flowchart showing the operation of the voice recognition device 100. [Figure 6] FIG. 13 is a diagram illustrating a process when an utterance is in English. [Figure 7] 1 is a diagram illustrating an example of a hardware configuration of a voice recognition device 100 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The embodiments of the present disclosure will be described with reference to the accompanying drawings. Whenever possible, the same parts are designated by the same reference numerals and redundant description will be omitted.

[0010] 1 is a block diagram showing the functional configuration of a speech recognition device 100 according to the present disclosure. As shown in the figure, the speech recognition device 100 includes a speech acquisition unit 101, a speech recognition unit 102, a non-language speech recognition unit 103 (non-language sound recognition unit), a score determination unit 104, and a result output unit 105. Each component will be described below.

[0011] The voice acquisition unit 101 is a part that acquires voice during a meeting, a lecture, or the like. For example, the voice acquisition unit 101 is a microphone. However, the voice acquisition unit 101 is not limited to this, and may be a part that acquires a voice signal transmitted by wire or wirelessly. The voice acquisition unit 101 detects a voice section from a voice waveform signal, and outputs each section to the voice recognition unit 102 and the non-language voice recognition unit 103.

[0012] The speech recognition unit 102 performs speech recognition processing on speech sounds or non-speech sounds in the speech section output from the speech acquisition unit 101 using a known language model and acoustic model, acquires recognition result text for each recognition unit, and derives the reading and reliability of the recognition result text. The speech recognition unit 102 outputs the recognition result text, its reading, and reliability for each recognition unit, such as an utterance unit, a sentence unit, a phrase unit, a word unit, a kana or phoneme unit, or a time unit.

[0013] This reliability is information indicating how much the recognition result in the case of speech recognition processing can be trusted, and is generally indicated between 0 and 1, but is not limited thereto, and may be expressed as an integer or between 0 and 100. It may also be a normalized numerical value. In the present disclosure, the reliability of the speech recognition result is calculated based on the reliability stored in the language model and the acoustic model, but is not limited thereto, and other known reliability deriving methods, such as end-to-end speech recognition, may be used.

[0014] The non-language speech recognition unit 103 is a part that performs recognition processing on non-language sounds using a known recognition model for non-language sounds for speech sounds or non-language sounds in the speech section output from the speech acquisition unit 101. This non-language speech recognition unit 103 generates a reliability for each event according to the type of non-language sound for each recognition time according to the recognition unit recognized by the speech recognition unit 102. For example, when the speech recognition unit 102 recognizes on a word-by-word basis, the speaking time for the recognized word becomes the recognition unit of the non-language speech recognition unit 103.

[0015] An event indicates the type of non-verbal sound and includes a positive event and a negative event. For example, in the present disclosure, non-verbal sounds include non-verbal sounds based on voices such as laughter, responses, nods, sighs, sneezes, coughs, etc., as well as sounds such as keyboard sounds and music sounds like BGM. For example, a positive event indicates that it is laughter, and a negative event indicates that it is not laughter. The non-verbal voice recognition unit 103 recognizes these non-verbal sounds and generates confidence levels for positive events and negative events.

[0016] The confidence level for an event is information indicating how much confidence can be placed in the recognition result when non-verbal voice recognition processing is performed. This confidence level is generally indicated between 0 and 1, but is not limited thereto, and may be represented by an integer or between 0 and 100. It may also be a normalized numerical value. This confidence level is obtained based on the confidence level stored in the recognition model for recognizing non-verbal sounds, but is not limited thereto, and other known methods for deriving the confidence level may be used.

[0017] FIG. 2 is an explanatory diagram showing the confidence level of the recognized result text of the voice and the confidence level for each event. As shown in the figure, assume that there is an utterance content "ahaha" (spoken as "ahahaha"). This indicates laughter.

[0018] The voice recognition unit 102 attempts to recognize the voice as a verbal sound, and thus recognizes the text data "a ha ha pai" (converted to Japanese Chinese characters) and the reading "a ha ha ha" (ahahaha). The voice recognition unit 102 derives the confidence level for the recognized result text recognized using the acoustic model and the language model.

[0019] On the other hand, the non-verbal voice recognition unit 103 derives the confidence level for each event. In FIG. 2, the confidence level corresponding to the presence or absence of laughter is derived. That is, for "ahaha" (spoken as "ahahaha"), the confidence level that it is not laughter and the confidence level that it is laughter are calculated. In FIG. 2, the confidence levels indicating that there is no laughter is 0.3 and that there is laughter is 0.7 are calculated.

[0020] In addition, the reliability of text data as laughter may be calculated. Also, as non-verbal sounds, the reliability of other types such as the presence or absence of coughing or the presence or absence of keyboard sounds may be calculated.

[0021] The non-verbal voice recognition unit 103 has recognition models for recognizing laughter, coughing, or keyboard sounds respectively, and can calculate the respective reliabilities based on these recognition models. Of course, in addition, recognition models for recognizing responses, nods, and sneezes may be provided. Also, a recognition model that outputs the reliability of each event for a plurality of events may be provided.

[0022] The score determination unit 104 is a part that determines which of the recognition result text and the event is appropriate based on the reliability of the recognition result text for the verbal sound recognized by the voice recognition unit 102 and the reliability for the event recognized by the non-verbal voice recognition unit 103.

[0023] The detailed processing will be described with reference to FIG. 2. In FIG. 2, it is assumed that “ahahaha” is input as the voice, and processing is performed in the voice recognition unit 102 and the non-verbal voice recognition unit 103 respectively. The score determination unit 104 compares the reliability of the recognition result text “ahahaha” (Japanese recognized here (an example of incorrect conversion)) of the voice recognition unit 102 with the reliabilities of the positive and negative events (no laughter / yes laughter) of the non-verbal voice recognition unit 103. In FIG. 2, the voice recognition unit 102 outputs a reliability of the recognition result text: 0.3. Also, the non-verbal voice recognition unit 103 outputs a reliability of the negative event (no laughter): 0.3 and a reliability of the positive event (yes laughter): 0.7.

[0024] The score determination unit 104 determines the recognition result text or event with the highest reliability. In Fig. 2, since the reliability of the positive event (laughing) is 0.7, which is the highest reliability, "ahahaha" is determined as an event indicating laughter.

[0025] As described above, the score determination unit 104 may determine the recognition result text or event based on the highest reliability, or may use values ​​adjusted by weighting for each reliability. For example, the score determination unit 104 may determine which event the utterance content is based on a value obtained by multiplying the reliability of a positive event or a negative event by a predetermined coefficient. More specifically, the score determination unit 104 may determine whether the utterance content is a non-speech sound by comparing a value obtained by multiplying the reliability of a positive event by 2 with the reliability of a negative event.

[0026] Moreover, the score determination unit 104 may determine whether a positive event in a non-speech sound is appropriate by multiplying the reliability of the positive event by 0.7, subtracting 0.1 from the reliability, and comparing the result with a threshold value. The coefficients and threshold values ​​may be fixed values ​​stored in advance in a memory or the like, or an input unit may be provided so that they can be input from the outside. Furthermore, the given coefficients and threshold values ​​may be varied by a predetermined formula. Moreover, the score determination unit 104 is not limited to comparing the reliability of the positive event and the negative event with two values, but may include other values, for example, may compare with three or more values. For example, laughter and coughing may be considered as positive events, no laughter and no coughing as negative events, and keyboard sounds may be considered as noise events, and the three reliability values ​​of the three events may be compared.

[0027] Such weighting adjustment is determined, for example, according to the attributes or type of the user who uses the speech recognition device 100 of the present disclosure, or the contents of the conference. For example, in the case of a conference content that is likely to cause laughter, laughter is easily recognized, but there are also conference contents in which this is not the case. In such cases, laughter is difficult to recognize as laughter. For such conferences or users, the adjustment can be made as described above to enable accurate recognition.

[0028] The result output unit 105 selects a highly reliable recognition result text based on the determination result by the score determination unit 104, or adds event tag information corresponding to the event and outputs it. When outputting, the result output unit 105 inputs information on the event determined by the score determination unit 104 and acquires event tag information corresponding to the event from a storage unit or the like. This storage unit or the like stores the event tag information in advance. The event tag information is, for example, information that graphically represents a non-verbal sound, and in the case of laughter, it is a mark that graphically represents laughter. The event tag information may be, for example, predefined text information.

[0029] Fig. 3 is a diagram showing an example of a processing result for a certain utterance. Fig. 3 shows, as an example of the processing result, a recognition result text, a recognition result morpheme, a recognition result text reliability, an event reliability (laugh), an event reliability (cough), an event reliability (keyboard sound), a judgment result, a result output, and a supplementary output. Also, the reliability and result output for each word are shown.

[0030] The following speech content is acquired, and the recognition result by the speech recognition unit 102 is obtained.

[0031] Speech: I'm, um, hahaha, kind of, (cough: Gogh), (keyboard sounds: clink clink), fine. The above utterance is in Japanese and is pronounced as watashiwa ano- hahaha nanka (gohho) kakakaka ii. In Japanese, the "wa" that comes after the subject is pronounced as wa.

[0032] Recognition result text: I'm a mother, I like Van Gogh, I like The above is the recognition result text converted to Japanese. Here, coughing and keyboard sounds are mixed in. This recognition result text is segmented into romanized characters watashi, wa, ano-, hahaha, nanka, gohho, kakakaka, and ii, and converted to Japanese. Here, for the utterance "watashiwa," the recognition results "watashi" and "wa" are obtained. The reliability of the recognition result text is derived as 0.95 and 0.91, respectively. Meanwhile, the reliability of each event for "watashi" by the non-language speech recognition unit 103 is as follows:

[0033] Confidence of laughter positive event: 0.23 Confidence of laughter negation event: 0.90 Confidence for positive cough event: 0.21 Confidence for negative cough event: 0.70 Confidence of positive keyboard event: 0.15 Confidence of negative keyboard sound event: 0.75 The reliability of each flag for "wa" by the non-language speech recognition unit 103 is as follows:

[0034] Confidence of laughter positive event: 0.35 Confidence of laughter negation event: 0.85 Confidence for positive cough event: 0.05 Confidence for negative cough event: 0.81 Confidence of positive keyboard event: 0.12 Confidence of negative keyboard sound event: 0.85 The score determination unit 104 selects the most reliable recognition result texts "watashi" (I) and "wa" (Wa) based on these reliability values, and the result output unit 105 outputs "watashiwa" (I) (watashiwa).

[0035] On the other hand, for the utterance: "ano-" and the recognition result text: "ano-", the speech recognition unit 102 determines the speech recognition reliability to be 0.9 and recognizes it as a filler.

[0036] Furthermore, for the utterance: "hahaha" and the recognition result text: "mother's voice" (text in which "hahaha" is recognized) (confidence level: 0.1), the non-language speech recognition unit 103 calculates a confidence level of a positive event (laughing voice): 0.7. Since the confidence level of the positive event (laughing voice) is high compared to the confidence level of the recognition result text and other positive events (coughing, keyboard sounds), the score determination unit 104 determines that the recognition result: "mother's voice" is laughter. The result output unit 105 outputs event tag information indicating that laughter has occurred as a supplementary output. Note that the result output unit 105 does not have to output the event tag information.

[0037] In the present disclosure, the score determination unit 104 determines that the recognition result text determined to be a filler based on the recognition result morpheme is a filler even if the recognition result text has a high reliability. Then, the result output unit 105 does not output the recognition result text as a result output. Note that, as necessary, event tag information indicating that it is a filler may be output as a supplementary output.

[0038] In the example of FIG. 3, the reliability is calculated for each word, and the judgment is made based on the reliability for each word, but the present invention is not limited to this. The score judgment unit 104 may change the recognition unit of the recognition result text output by the speech recognition unit 102 and the non-language speech recognition unit 103 to a judgment unit for score judgment and make a judgment. For example, in FIG. 3, the speech recognition unit 102 and the non-language speech recognition unit 103 calculate the reliability for each word, but the score judgment unit 104 may integrate the reliability for each phrase or sentence, and make a judgment based on the reliability integrated for each phrase or sentence. By changing the score judgment range in this way, for example, the position where the event tag information indicating the non-language sound is added can be changed, making the text easier to read. For example, when the score is judged for each sentence, the event tag information is added to the end of the sentence.

[0039] Fig. 4 is a diagram showing an outline of the processing contents when the score determination unit is changed to the sentence unit. For the sake of convenience, the description of the event reliability, etc. is simplified compared to Fig. 3. Assume that the following speech content is input and the recognition result text is obtained.

[0040] Utterance: I, um, (laughing: hahaha), kind of, (cough: gogh), (keyboard noises: cack-cack), good. The above utterance is in Japanese and is pronounced as watashiwa ano- hahaha nanka (gohho) kakakaka ii. In Japanese, the "wa" that comes after the subject is pronounced as wa. Recognition result text: I, um, Mother, something, Van Gogh, or something, good The above is the recognition result text converted to Japanese. Coughing and keyboard sounds are mixed in. This recognition result text, in romanized notation, shows that it has been segmented into watashi, wa, ano-, hahaha, nanka, gohho, kakakaka, and ii and converted to Japanese. Here, the score determination unit 104 performs score determination on a sentence-by-sentence basis. That is, the score determination unit 104 sums up the speech recognition reliability and the non-language speech recognition reliability in one sentence of the recognition result text. For example, in the example of FIG. 4, the score determination unit 104 calculates the total reliability of the recognition result text in that one sentence and the total reliability of the events (respective reliability of the positive event and the negative event). Based on this total, the score determination unit 104 determines whether the recognition result text is appropriate and whether an event exists. In the example of FIG. 4, since the total reliability of the recognition result text and the positive event is equal to or greater than a predetermined value, the score determination unit 104 determines that the recognition result text includes speech, laughter, coughing, or keyboard sounds.

[0041] Then, the score determination unit 104 outputs each recognition result text whose reliability is equal to or greater than a predetermined value, such as "I," "I," "um," "something," and "good," as well as outputting event information (laughing, coughing, keyboard sounds), to the result output unit 105. The result output unit 105 acquires event tag information from the event information and outputs it together with the recognition result text whose reliability is equal to or greater than a predetermined value.

[0042] This makes it possible to output event tag information on a sentence-by-sentence basis and add event tag information to the end of the recognition result text, resulting in easy-to-read sentences.

[0043] Next, an operation of the speech recognition device 100 of the present disclosure will be described. Fig. 5 is a flowchart showing the operation of the speech recognition device 100. The speech acquisition unit 101 acquires a speech waveform signal (S101), performs speech section detection from the speech waveform signal, and outputs the speech (or other sound) in the speech section as a speech signal to the speech recognition unit 102 and the non-language speech recognition unit 103 (S102).

[0044] The speech recognition unit 102 performs speech recognition processing on the speech signal and outputs the recognition result text, reading, and reliability (S103). In addition, the non-language speech recognition unit 103 performs non-language speech recognition processing on the speech signal and outputs the reliability of each event for each recognition target time (S104).

[0045] The score determination unit 104 determines the validity of the recognition result text or the event for each recognition target based on the reliability of the recognition result text by the speech recognition processing and the reliability of each event recognized by the non-language speech recognition (S105).

[0046] The result output unit 105 selects appropriate recognition result text from the recognition result text based on the determination result, or obtains and outputs event tag information (S106).

[0047] By performing such processing, speech and non-speech sounds can be recognized during a meeting, etc.

[0048] Next, application examples of other languages ​​will be described. The above speech examples are for Japanese, but similar processing is naturally possible for other languages. Figure 6 is a diagram showing processing when the speech is in English. Note that the description is simplified for convenience of explanation. In Figure 6, the following speech is made.

[0049] Utterance: I go (laughing: hahaha) to (cough: off coff cough) (keyboard noises: clatter) school. Recognition result text:I go ah the her head to Costoco caca grata. Here, the speech recognition reliability of the recognition result text "I", "go", "to", and "school" recognized by the speech recognition unit 102 is high.

[0050] On the other hand, the recognition result texts "the her head", "Costoco", and "caca gratta" recognized by the speech recognition unit 102 have low recognition result text reliability but high event reliability. For example, the recognition result text "the her head" has a high laughter event reliability. This is because the laughter "hahaha" is being recognized as a speech sound.

[0051] The same is true for the recognition result texts "costoco" and "caca grata", which are derived with low speech confidence as a result of attempting to recognize a cough or keyboard sounds, respectively, as speech sounds. On the other hand, since these utterances are coughs and keyboard sounds, it is necessary not to output the recognition result text for them.

[0052] In the example of FIG. 6, the following result is output based on the recognition result text confidence and event confidence:

[0053] Result output:I go to school. In this way, it is possible to prevent non-speech sounds from being output even for English speech recognition.

[0054] Next, the effects of the speech recognition device 100 of the present disclosure will be described. The speech recognition device 100 includes a speech acquisition unit 101 (sound information acquisition unit) that acquires a speech waveform signal (sound information) in a predetermined unit obtained by speech section detection, and a sound information processing unit that outputs a result based on speech recognition processing when the speech waveform signal, which is the sound information, is speech, and performs processing to not output a result based on the speech recognition processing when the sound information is non-language. In the present disclosure, the sound information processing unit is composed of a speech recognition unit 102, a non-language speech recognition unit 103, a score determination unit 104, and a result output unit 105.

[0055] With this configuration, it is possible to output the recognition results of speech, which is language, while preventing the recognition results of non-language sounds from being output.

[0056] The speech information processing unit does not necessarily have to be composed of the speech recognition unit 102, the non-language speech recognition unit 103, the score determination unit 104, and the result output unit 105. It may be configured to output the reliability of the recognition result by the speech recognition unit 102 and the non-language speech recognition unit 103 together with the recognition result. In this case, the output destination performs a determination process according to the reliability.

[0057] In addition, in the present disclosure, the result output unit 105, which is one component of the sound information processing unit, outputs a result based on the non-language sound recognition processing for non-language when the voice waveform signal (sound information) is non-language.

[0058] With this configuration, when non-language speech is processed by speech recognition, a recognition result with unclear meaning is obtained, but by outputting the result of non-language speech recognition processing, a recognition result that is easy to understand can be obtained. In general, non-language sounds are often pronounced in a way that makes it difficult to add furigana or to make words out of them. Therefore, if words such as laughter and / or furigana are registered in a dictionary for speech recognition processing, there is a new problem of increased costs due to the number of patterns, but this problem can be solved by providing a separate non-language speech recognition process.

[0059] In the speech recognition device 100 of the present disclosure, the sound information processing unit includes a speech recognition unit 102 that recognizes a speech waveform signal (sound information) as speech, and a non-speech speech recognition unit 103 that recognizes the speech waveform signal (sound information) as a non-speech sound. The sound information processing unit outputs information based on each recognition result, such as a reliability, to an external terminal.

[0060] In the above embodiment, the reliability was determined and the result was output in accordance with the determination within the speech recognition device 100, but these processes may be requested from an external terminal. That is, as a process for not outputting the result based on the speech recognition process, the speech recognition device 100 outputs the recognition result text from the speech recognition process and its reliability, as well as the recognition result (event, etc.) from the non-language speech recognition process and its reliability to the external terminal. The external terminal can obtain the recognition result text, etc. based on this information.

[0061] In the speech recognition device 100 of the present disclosure, the sound information processing unit further includes a score determination unit 104 that determines the recognition results of the speech recognition unit 102 and the non-language speech recognition unit 103, and a result output unit 105 that processes and outputs the recognition result text by the speech recognition unit 102 in accordance with the determination. That is, based on the determination result by the score determination unit 104, the result output unit 105 does not output the non-language sound part of the recognition result text, and outputs only the language part.

[0062] This makes it possible to obtain easy-to-read recognition result text because the recognition result text for non-speech sound parts is not output.

[0063] In the speech recognition device 100 of the present disclosure, the speech recognition unit 102 derives speech sound confidence for the sound information being speech, i.e., confidence for the recognition result text, and the non-language speech recognition unit 103 derives non-language sound confidence for the sound information being non-language, i.e., confidence for the event. Then, the score determination unit 104 determines the recognition results (recognition result text and each event) by the speech recognition unit 102 and the non-language speech recognition unit 103 based on these confidences (speech sound confidence and non-language sound confidence).

[0064] Here, the reliability of an event indicating the non-speech sound reliability indicates the reliability that the speech waveform signal (sound information) is a non-speech sound and the reliability that the speech waveform signal (sound information) is not a non-speech sound, i.e., indicates a positive event and a negative event, respectively.

[0065] Then, the score determination unit 104 performs a weighting process on at least one of the reliability of the positive event and the reliability of the negative event, and performs a determination process.

[0066] By carrying out such weighting processing, it is possible to make a determination according to the attributes or type of the user and the contents of the conference.

[0067] The non-verbal speech in the above is at least one of laughter, nodding, interjections, coughing, sneezing, and keyboard sounds, but is not limited to these.

[0068] In addition, in the present disclosure, the speech recognition unit 102 performs speech recognition in a predetermined speech recognition unit (sentence unit, phrase unit, word unit, etc.), and the non-language speech recognition unit 103 performs non-language speech recognition in time units according to the predetermined speech recognition unit.

[0069] This enables recognition of non-speech sounds in accordance with the units of speech recognition.

[0070] The score determination unit 104 may determine the recognition result in a determination unit different from the speech recognition unit. For example, speech recognition and non-language speech recognition may be performed in units of words, and the score may be determined in units of sentences.

[0071] The block diagrams used in the description of the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized by using one device that is physically or logically combined, or may be realized by using two or more devices that are physically or logically separated and directly or indirectly connected (for example, by wire, wireless, etc.). The functional block may be realized by combining the one device or the multiple devices with software.

[0072] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocation, mapping, assignment, etc. For example, a functional block (component) that performs the function of transmission is called a transmitting unit or a transmitter. In either case, as described above, there is no particular limitation on the method of realization.

[0073] For example, the voice recognition device 100 according to an embodiment of the present disclosure may function as a computer that performs processing of the voice recognition method according to the present disclosure. Fig. 7 is a diagram showing an example of a hardware configuration of the voice recognition device 100 according to an embodiment of the present disclosure. The above-mentioned voice recognition device 100 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0074] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the speech recognition apparatus 100 may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0075] Each function in the speech recognition device 100 is realized by loading a specific software (program) onto hardware such as a processor 1001 and a memory 1002, causing the processor 1001 to perform calculations, control communications via a communication device 1004, and control at least one of reading and writing of data in the memory 1002 and the storage 1003.

[0076] The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned speech recognition unit 102 and non-language speech recognition unit 103, etc. may be realized by the processor 1001.

[0077] Moreover, the processor 1001 reads out a program (program code), a software module, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to the programs. As the programs, a program that causes a computer to execute at least a part of the operations described in the above-mentioned embodiment is used. For example, the voice recognition unit 102 may be realized by a control program stored in the memory 1002 and running on the processor 1001, and other functional blocks may be similarly realized. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunication line.

[0078] The memory 1002 is a computer-readable recording medium, and may be composed of at least one of, for example, a Read Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), a Random Access Memory (RAM), etc. The memory 1002 may be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store a program (program code), software modules, etc. that are executable to implement a speech recognition method according to an embodiment of the present disclosure.

[0079] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, and the like. Storage 1003 may be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0080] The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., in order to realize at least one of, for example, Frequency Division Duplex (FDD) and Time Division Duplex (TDD). For example, the above-mentioned voice acquisition unit 101, etc. may be realized by the communication device 1004. The voice acquisition unit 101 may be implemented as a transmission unit and a reception unit that are physically or logically separated.

[0081] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0082] In addition, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0083] Furthermore, the speech recognition device 100 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0084] The notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB))), other signals, or a combination thereof. In addition, the RRC signaling may be called an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0085] The order of the steps, sequences, flow charts, etc. of each aspect / embodiment described in this disclosure may be changed unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0086] The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

[0087] The determination may be based on a value represented by a single bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0088] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched according to execution. In addition, notification of specific information (e.g., notification that "X is the case") is not limited to being done explicitly, but may be done implicitly (e.g., not notifying the specific information).

[0089] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described in the present disclosure. The present disclosure can be implemented as modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0090] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0091] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave, etc.), then these wired and / or wireless technologies are included within the definition of transmission media.

[0092] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0093] In addition, the terms described in this disclosure and the terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and the symbol may be a signal (signaling). Also, the signal may be a message. Also, the component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0094] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), and the like. In addition, "judgment" and "decision" can include considering resolving, selecting, choosing, establishing, comparing, etc., to be a "judgment" or "decision." In other words, "judgment" and "decision" can include considering some action to be a "judgment" or "decision." In addition, "judgment" can be interpreted as "assuming," "expecting," "considering," etc.

[0095] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0096] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0097] It doesn't mean you have to.

[0098] When "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Further, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0099] In this disclosure, where articles have been added due to translation, such as, for example, a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0100] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0101] 100: speech recognition device, 101: speech acquisition unit, 102: speech recognition unit, 103: non-language speech recognition unit, 104: score determination unit, 105: result output unit.

Claims

1. a sound information acquisition unit that acquires sound information in predetermined units; a sound information processing unit that outputs a result based on a speech recognition process when the sound information is a speech sound, and that performs processing to not output a result based on the speech recognition process when the sound information is a non-speech sound; Equipped with The sound information processing unit includes: a speech recognition unit that recognizes the sound information as speech sounds; a non-speech sound recognition unit that recognizes the sound information as a non-speech sound; a determination unit that determines the recognition results of the speech recognition unit and the non-speech sound recognition unit; a result output unit that processes and outputs a recognition result by the speech recognition unit in accordance with the determination by the determination unit; having The speech recognition unit calculates a speech sound reliability for the sound information being a speech sound, the non-speech sound recognition unit calculates a non-speech sound reliability for the non-speech sound in the sound information, the determination unit determines recognition results by the speech recognition unit and the non-speech sound recognition unit based on the speech sound reliability and the non-speech sound reliability; the non-speech sound reliability indicates a reliability that the sound information is a non-speech sound and a reliability that the sound information is not a non-speech sound. Speech recognition device.

2. The sound information processing unit includes: The speech recognition device according to claim 1 , wherein, when the sound information is a non-speech sound, a result based on a non-speech sound recognition process for the non-speech sound is output.

3. the determination unit performs a weighting process on at least one of the reliability that the sound information is a non-speech sound and the reliability that the sound information is not a non-speech sound, and performs a determination on a recognition result.

3. The speech recognition device according to claim 1.

4. The voice recognition unit performs voice recognition in a predetermined voice recognition unit, the non-language sound recognition unit performs non-language speech recognition in a time unit corresponding to the predetermined speech recognition unit; The speech recognition device according to any one of claims 1 to 3.

5. The voice recognition unit performs voice recognition in a predetermined voice recognition unit, the non-language sound recognition unit performs non-language speech recognition in a time unit corresponding to the predetermined speech recognition unit; the determination unit determines the recognition result in a determination unit different from the speech recognition unit; The speech recognition device according to any one of claims 1 to 3.

6. 6. The speech recognition device according to claim 1, wherein the non-speech sound is at least one of laughter, a back-channel sound, a nodding sound, a cough, a sneeze, and a keyboard sound.

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