16 results about "Spoken Language Ability" patented technology

A system and method is described for internationalization of web pages by extracting translatable content from extensible mark-up language (XML), or similar data-centric meta-language representations of web pages or data used to build web pages. The extracted translatable content is stored in a translation task repository (TTR) accessible by the web developer and the translator. The XML representation is then modified to include selection control logic to select the appropriate translations for insertion into the final web page. The translator accesses the TTR to translate the appropriate content and saves the translations back to the TTR associated with the original translatable data. The translations are obtained from the TTR as selection cases for the selection control logic of the XML representation. As the XML is converted into the web source code, the selection logic and translations are embedded therein facilitating building the web site in multiple different languages.

The invention discloses a spoken language evaluation system and method based on multi-dimensional factors, and relates to the technical field of data processing, and the method comprises the steps: obtaining an audio text, a phoneme list and a phoneme time boundary list corresponding to voice data according to a pre-trained text phoneme recognition model; calculating a fluency score according to the phoneme list and the phoneme time boundary list; obtaining a semantic score of the audio text through a pre-trained semantic model; obtaining a grammar score of the audio text through a pre-trained grammar model; matching the audio text and the answer text according to a text matching method based on an editing distance to obtain a text matching score; the pronunciation score of the audio data is finally obtained through the phoneme confidence of the phoneme list; dynamically adjusting the weight of the score of each dimension according to the length of the audio text, and calculating a final spoken language evaluation score; through multi-dimensional evaluation, the spoken language ability is comprehensively evaluated according to dynamic weight adjustment of the scene, and comprehensiveness, fairness, accuracy and high efficiency of spoken language evaluation are achieved.

The invention relates to the technical field of teaching equipment, in particular to a multimedia teaching interaction device based on artificial intelligence, which comprises a practice room, a multimedia teaching interaction device and a multimedia teaching interaction device, the learning desk is arranged in the exercise room, and a data processing assembly is arranged on the learning desk; the first seat is arranged in the exercise room and located on the rear side of the study desk; the arc-shaped isolation plate is arranged in the exercise room, and the width of the arc-shaped isolation plate is larger than that of the opening; the support frame is arranged above the front side of the study desk; the first driver is arranged on the arc-shaped isolation plate and located in the exercise room, and the output end of the first driver is fixedly connected with the supporting frame so as to drive the supporting frame to be close to or away from the arc-shaped isolation plate; and the two displays are arranged on the support frame. According to the invention, artificial intelligence and English aided teaching are combined with each other, a teacher does not need to participate in a learning process in which students are matched with an artificial intelligence program, the students can freely practice oral language, and the oral English ability is improved.

The invention discloses a hearing aid and spoken language partner training integration method and system based on hearing loss and compensation, and belongs to the crossing field of hearing aid and artificial intelligence language learning. The method is cooperatively executed by a Bluetooth headset and a mobile terminal application program, and comprises the following steps: establishing a personalized hearing loss curve of a user; the equipment has an intelligent hearing aid mode and a spoken language partner training mode. And in the hearing aid mode, real-time compensation playing is carried out on the environment sound based on the curve. In the partner training mode, three partner training processes of real-time, preset scene and word generation scene are provided, and adaptation processing is performed on the generated partner training audio based on the same hearing curve, so that key teaching phonemes are ensured to be clear and audible. The core innovation lies in that the system dynamically adjusts the compensation parameter of the hearing loss curve by analyzing the pronunciation accuracy of the user in partner training, and realizes collaborative optimization and personalized improvement of the hearing aid effect and the spoken language ability.

The invention relates to a multi-mode self-adaptive oral English training method, system and device and a medium. The method comprises the following steps: acquiring multi-modal data; extracting features of spoken language ability corresponding to the multi-modal data to obtain a feature vector group; performing standardization processing on the feature vector group to obtain an index matrix; inputting the index matrix into a pre-trained attention mechanism model to obtain a spoken language ability portrait corresponding to the user; taking the spoken language ability portrait as an index, and matching corresponding teaching resources in a preset training resource library to obtain modular training resources; the modular training resources are courses corresponding to capability defects of the user; and generating a training scheme of the corresponding user based on the modular training resources. By adopting the method, multi-modal data can be introduced to carry out quantitative evaluation on the spoken language ability of the user, and the reasons of errors in the spoken language dialogue process of the student can be effectively judged.

The application discloses a hearing aid and oral language practice integrated method and system based on hearing loss and compensation, and belongs to the cross field of hearing aid and artificial intelligence language learning. The method is cooperatively executed by a Bluetooth earphone and a mobile terminal application program, and comprises the following steps: a user's personalized hearing loss curve is established; the device has a dual mode of intelligent hearing aid and oral language practice. In the hearing aid mode, the environment sound is compensated and played in real time based on the curve. In the practice mode, three practice processes of real-time, preset scene and new word scene are provided, and the generated practice audio is adaptively processed based on the same hearing curve, so that the key teaching phonemes are clear and audible. The core innovation is that the system dynamically adjusts the compensation parameters of the hearing loss curve by analyzing the pronunciation accuracy of the user in the practice, so that the hearing aid effect and oral language ability are cooperatively optimized and individually improved.

The present invention discloses a multi-dimensional oral ability recognition and quantification method and system, which relate to the technical field of oral ability recognition, including: performing recognition preprocessing on spoken audio and obtaining language type and basic clarity score; using fluency analysis method and vocabulary analysis method to obtain fluency score and vocabulary structure score; analyzing the fluency score and vocabulary score based on the basic clarity score, and obtaining the oral ability score; the present invention is used to solve the problem that the existing oral ability recognition method lacks an evaluation method for the fluency of oral expression and the rationality of word structure, resulting in relatively one-sided recognition, and thus unable to perform differentiated recognition of oral abilities under the same semantics, and further unable to accurately perform comprehensive evaluation of the user's oral ability.

The invention discloses a page interaction method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, the method comprises the following steps: displaying a learning interaction page corresponding to a learning assistant, the learning interaction page comprising a language dialogue component; in response to a trigger operation for the language dialogue component, displaying a voice dialogue interface, the voice dialogue interface comprising a scene selection control; in response to a triggering operation for the scene selection control, determining a target scene; and in response to first voice dialogue information generated according to the target scene, generating target dialogue information matched with the first voice dialogue information. According to the technical scheme, the learning assistant can be used for simulating a real spoken language communication scene, the spoken language ability and the hearing ability can be exercised, and the foreign language learning experience of the user is improved.

The application discloses a spoken language evaluation system and method based on multiple dimensions, and relates to the technical field of data processing.The system comprises the following steps: obtaining audio text, a phoneme list and a phoneme time boundary list corresponding to speech data according to a pre-trained text phoneme recognition model; calculating a fluency score according to the phoneme list and the phoneme time boundary list; obtaining a semantic score of the audio text through a pre-trained semantic model; obtaining a syntax score of the audio text through a pre-trained syntax model; matching the audio text and an answer text according to a text matching method based on an edit distance to obtain a text matching score; obtaining a pronunciation score of the audio data finally through phoneme confidence of the phoneme list; dynamically adjusting the weight of each dimension score according to the length of the audio text, and calculating a final spoken language evaluation score; and the application comprehensively evaluates the spoken language ability through multi-dimensional evaluation and dynamic weight adjustment according to the scene, and realizes the comprehensiveness, fairness, accuracy and high efficiency of the spoken language evaluation.

The invention discloses a multi-dimensional spoken language ability recognition quantification method and system, and relates to the technical field of spoken language ability recognition, and the method comprises the steps: carrying out the recognition preprocessing of a spoken language audio, and obtaining a language type and a basic clear score; using a fluency analysis method and a vocabulary analysis method to obtain a fluency score and a vocabulary structure score; analyzing the smooth score and the vocabulary score based on the basic clear score, and obtaining a spoken language ability score; the method is used for solving the problems that in an existing spoken language ability recognition method, due to the fact that a judgment method for the fluency of spoken language expression and the rationality of word structures is lacked, recognition is one-sided, differential recognition cannot be conducted on spoken language ability under the same semantics, and the recognition efficiency is poor. And thus, the spoken language ability of the user cannot be comprehensively judged accurately.

The present invention provides a method, system and storage medium for evaluating oral ability, and relates to the technical field of oral evaluation. The present invention collects and preprocesses ambient audio signals from a recording environment to generate an ambient noise power for reflecting the power value of ambient noise, performs correlation analysis on the signals, and generates an ambient noise impact assessment index reflecting the degree of influence of ambient noise on the recording signal. At the same time, a standard oral set is set, and oral parameters of oral ability training subjects are collected. After analysis, a speech speed accuracy assessment index is generated for evaluating the relationship between the speaking speed of a person to be tested and the output accuracy of a device. After comprehensive analysis, a comprehensive oral evaluation index is generated for reflecting a comprehensive evaluation based on the current environment and the speaking speed of the person to be tested. After comparison with an accuracy threshold, the accuracy level of the oral ability evaluation of the person to be tested is output to evaluate the accuracy of the oral ability evaluation.

The application belongs to the technical field of information processing, and particularly relates to a business English oral practice method, which specifically comprises the following steps: collecting a voice signal of a user, and extracting acoustic features of the voice signal by using an acoustic model to obtain spectral feature data; performing phoneme decoding on the spectral feature data to obtain text transcription data and confidence score data of each word, and determining accurate semantic information of the voice signal; determining a business scenario category to which the voice signal belongs and evaluation standards of respective indexes corresponding to the business scenario category based on the accurate semantic information; and evaluating the voice signal based on the evaluation standards of the respective indexes in the business scenario category, and generating a corresponding improvement scheme. The method realizes intelligent and personalized evaluation of business English oral ability, and can effectively improve the business English communication ability of students.

The invention relates to the technical field of speech recognition, in particular to a foreign language pronunciation evaluation system and method based on machine learning, and the method comprises the steps: obtaining a language selected by a user and a pronunciation evaluation mode, and obtaining the pronunciation data of the user; processing the pronunciation data by adopting a corresponding sub-evaluation model in a preset foreign language pronunciation evaluation model to obtain a comprehensive evaluation result containing a plurality of evaluation indexes; and when it is detected that information that the score of the evaluation index is smaller than a preset standard score exists in the comprehensive evaluation result, a preset strategy generation model is adopted to analyze and match the comprehensive evaluation result, so that a learning strategy meeting user requirements is obtained. By means of the method, the problems that in the foreign language pronunciation evaluation technology, a comprehensive score calculation mode lacks emphasis, the spoken language ability of a practicer cannot be reflected in a targeted mode, and an improvement scheme cannot be given are solved.

The present application provides a spoken language evaluation method and system based on text and speech recognition, including: respectively recognizing the text and speech of the user to be evaluated to obtain a first evaluation score and a second evaluation score; determining a first evaluation score difference and a second evaluation score difference under each evaluation dimension based on the first evaluation score and the second evaluation score; when the first evaluation score difference is greater than a first threshold score or any second evaluation score difference is greater than a second threshold score, determining a target user from the spoken language evaluation database; re-evaluating the user to be evaluated through the spoken language evaluation system used by the target user to obtain a third evaluation score and a fourth evaluation score; determining the final spoken language evaluation score of the user to be evaluated based on the first evaluation score, the second evaluation score, the third evaluation score, and the fourth evaluation score. In this way, by combining the speech and documents of the user to be evaluated for spoken language evaluation, the spoken language ability of the user to be evaluated can be evaluated more comprehensively.

This invention provides a spoken language situational dialogue simulation system, relating to the field of machine question-answering technology. It includes a spoken language proficiency data acquisition module: collecting user spoken language proficiency feature data; an ability calculation module: preprocessing the collected spoken language proficiency feature data to obtain a spoken language ability index; a first speech rate calibration module: setting an initial speech rate benchmark and obtaining a speech rate calibration value; a trend analysis module: calculating the change value between the spoken language ability index corresponding to adjacent fixed time windows; and a second speech rate calibration module: calculating the average change value of spoken language ability within a judgment period, determining whether a secondary adjustment of the speech rate calibration value is needed, and selecting to apply either an upward or downward speech rate adjustment coefficient to adjust the speech rate calibration value. This invention avoids user frustration caused by excessively fast speech rate and also avoids limiting user improvement potential by excessively slow speech rate, thus allowing each learner to conduct efficient spoken language training at their most suitable pace.

The invention provides a method and system for evaluating spoken language ability and a storage medium, and relates to the technical field of spoken language evaluation.The method comprises the steps that environment audio signals of an input environment are collected and preprocessed, environment noise power used for reflecting the power value of environment noise is generated, correlation analysis is conducted on the environment noise power, and the spoken language ability is evaluated; the method comprises the following steps: generating an environmental noise influence evaluation index which reflects the influence degree of environmental noise on an input signal, setting a standard spoken language set, collecting spoken language parameters of a spoken language ability training object, analyzing the spoken language parameters, and generating a speech speed accuracy evaluation index which is used for evaluating the relationship between the speech speed of a to-be-tested person and the equipment output accuracy; after comprehensive analysis, a spoken language evaluation comprehensive index used for reflecting comprehensive evaluation in combination with the current environment and the speech speed of the to-be-tested person is generated and compared with an accurate threshold value, then the spoken language ability evaluation accuracy level of the to-be-tested person is output, and the spoken language ability evaluation accuracy is evaluated.