33 results about "Speech disorder" patented technology

The invention provides an AI-powered speech, language therapy, and language learning system that utilizes Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) to deliver personalized, real-time therapy and learning for individuals with speech disorders or those seeking to improve language proficiency. The system analyzes user speech, language comprehension, and facial expressions, providing immediate feedback on pronunciation, fluency, articulation, and sentence structure. A GAN-generated avatar interacts with the user, mimicking human expressions and offering dynamic, engaging sessions. The platform adapts exercises based on user performance using personalized algorithms to ensure continuous progress. Additionally, it securely stores user data in compliance with privacy regulations, making it accessible through web and mobile platforms. This invention improves upon existing speech therapy and language learning solutions by integrating real-time visual and auditory feedback with AI-driven personalization, offering a more immersive and effective experience.

The present application relates to the technical field of speech recognition, in particular to a speech recognition and training method for speech disorder rehabilitation patients. The present application first extracts the text in the speech data, and obtains the problem words and the standard scores of the text of the patient's pronunciation; further constructs the feature vector of each text, and classifies the standard pronunciation text and the text of the patient's pronunciation; further obtains the pronunciation disorder degree of each problem word of the patient according to the text difference and the feature vector difference between the pronunciation word class cluster and the corresponding standard word class cluster, in combination with the standard score of the text of the patient and the proportion of the problem word; finally, according to the distribution of the problem word in the sentence, in combination with the pronunciation disorder degree and the appearance frequency of the problem word, the pronunciation difficulty of each problem word is obtained, the personalized pronunciation disorder of the patient is accurately identified, and personalized training schemes are formulated for relevant personnel, thereby providing support for improving the intervention effect.

A tone evaluation rehabilitation training device and system, the device comprises a flexible cap body and a bandage, the cap body is integrated with a 10-channel electrode plate to accurately cover language brain areas on both sides, electroencephalogram signals are transmitted in real time through Bluetooth 5.0, and a noise reduction earphone is used for providing sound output for a patient. The microphone is used for collecting audio of a patient and transmitting the audio to the processing module for analysis, the system dynamically regulates acoustic stimulation based on the neural activation feedback module, high-precision tone recognition is achieved in combination with audio preprocessing, SpecAugment data enhancement, a CNN-Transform mixed model and an attention mechanism, and training efficiency is optimized through pre-training model fine tuning and Warmup learning rate scheduling. An initial consonant-vowel-tone three-dimensional confusion probability table is introduced to divide interference intensity grades, high and low interference task paths are dynamically switched through a semantic error rate, and an evaluation module generates a four-tone accuracy rate, an F0 curve comparison graph and a personalized rehabilitation scheme. The recognition precision is improved, the training period of special crowds is shortened, and full-period self-adaptive rehabilitation is provided for preschool children to speech disorder patients.

To provide a system and method for providing speech instructions based on classification of speech of language communication from a user.SOLUTION: A computing system can generate speech characteristics of a first verbal communication using a first audio sample from a user. The computing system can determine a first speech classification of the first verbal communication based on the speech characteristics from a plurality of speech classifications. The computing system can select one action from a plurality of actions that includes modifying one or more speech characteristics that define a user's speech based on the first speech classification. The computing system can provide instructions to present a message prompting the user to perform the speech defined by this one action selected from the plurality of actions. The efficacy of the medication that the user is taking to address the condition can be increased.SELECTED DRAWING: Figure 1

The present invention relates to an apparatus (10) for the self-administration of therapies for the treatment of speech disorders comprising a stimulus delivery device (11,12) with a horizontal development substantially curved along an angular portion so as to outline a portion of a circle, comprising at least one panel (11) for the delivery of visual stimuli spatially distributed in an angular range substantially equal to the angular portion of horizontal development of the stimulus delivery device (11,12); at least one acoustic source (12) for the delivery of acoustic stimuli spatially distributed in an angular range substantially equal to the angular portion of horizontal development of the stimulus delivery device (11,12); and a local electronic processing unit configured to control the generation of visual and acoustic stimuli by means of the at least one panel (11) and the at least one acoustic source (12), and characterized in that it comprises at least one first acoustic detector (15) for detecting a reproduction of a verbal element produced by the patient, wherein the at least one first acoustic detector (15) is carried by the at least one panel (11) and positioned so as to detect acoustic signals generated in a space inside the portion of circle outlined by the stimulus delivery device (11,12).

The application provides a Chinese spoken language intention recognition brain-computer interface system based on a sound-meaning integration double model, belongs to the technical field of biomedical engineering, and relates to language brain-computer interface technology. Taking sound-meaning integration as the core, the stereotactic intracranial electroencephalogram (sEEG) technology is adopted to collect neural signals of the brain articulatory motor coding area and the semantic concept organization coding area. The system comprises a voice initiation decoder, a speech decoder, a semantic decoder and a Chinese word speech-semantic fusion synthesizer, the target decoder is constructed by extracting high gamma band features of key brain areas of the frontal lobe (left inferior frontal gyrus, premotor cortex, etc.), the temporal lobe (anterior temporal lobe, dorsolateral temporal lobe, etc.). At the same time, a visual and auditory induction training paradigm is matched, three tasks of listening to sound to group words, looking at words to group words and word association are set, and the subjects are supported to generate words independently. The system effectively solves the homonym and near homonym word ambiguity problem in Chinese spoken language recognition, and provides a precise interactive tool for ALS and other speech disorder patients.

The invention provides a Chinese spoken language intention recognition brain-computer interface system based on pronunciation-meaning integration double models, belongs to the technical field of biomedical engineering, and relates to a language brain-computer interface technology. Sound-sense integration is taken as a core, and a stereotactic intracranial electroencephalogram (sEEG) technology is adopted to acquire neural signals of a brain phonetic motion coding region and a semantic concept organization coding region. The system comprises a sound production starting decoder, a voice decoder, a semantic decoder and a Chinese word voice-semantic fusion synthesizer, and a target decoder is constructed by extracting high gamma wave band characteristics of key brain regions of frontal lobe (left subfrontal gyrus, anterior cortex of motion and the like) and temporal lobe (anterior temporal lobe, dorsal lateral temporal lobe and the like). Meanwhile, an audio-visual induction training normal form is matched, three tasks of listening word combination, character reading word combination and vocabulary association are set, and subjects are supported to autonomously generate vocabularies. The system effectively solves the problem of ambiguity of homophonous and near-phonetic words in spoken Chinese recognition, and provides an accurate interaction tool for speech disorder patients such as ALS and the like.

The present disclosure relates to a speech disorder detection method, device, equipment and readable storage medium. By acquiring standard audio-visual materials, in response to the pronunciation operation of the to-be-detected object to the standard audio-visual materials, multi-modal pronunciation data is collected, audio acoustic features are extracted based on the pronunciation audio, video visual features are extracted based on the video of the face and oral cavity activity, the audio acoustic features, the video visual features and the demographic information coding data are fused to obtain a fusion feature vector, and based on the fusion feature vector and a pre-trained prediction model, a speech disorder detection result of the to-be-detected object is obtained. Compared with the prior art, the embodiment of the present disclosure can improve the accuracy and comprehensiveness of speech disorder detection, improve the diagnosis efficiency, reduce the dependence on professionals, reduce the burden of medical resources, and clearly determine the specific type of pronunciation problem, thereby providing a scientific basis for subsequent individualized intervention treatment.

[Problem] To realize a technique capable of improving quality of life for a subject such as a patient who is impaired in vocalization. [Solution] An information processing device 1 includes a moving image data acquisition unit 11a, a preprocessing unit 11b, a feature amount acquisition unit 11c, a learning model generation unit 11d, an utterance content estimation unit 11e, and a substitute voice output unit 11f. The moving image data acquisition unit 11a acquires a moving image including lips of a subject. The preprocessing unit 11b extracts a lip portion in the moving image. The feature amount acquisition unit 11c acquires a feature amount of the extracted lip portion. The utterance content estimation unit 11e estimates an utterance content of the subject corresponding to the lip portion extracted by the preprocessing unit 11b, on the basis of a learning model that has been machine-learned in advance by associating a feature amount of a lip portion in a moving image for machine learning including the movement of lips during utterance with the utterance content uttered by the lip portion.

The present invention provides a method and apparatus for generating speech synthesis information, which relates to the technical field of speech data processing and can be used in the financial field or other technical fields. The method includes: obtaining a target video, and extracting human joint point features and lip language features according to the target video; the target video includes real-time images of customers with speech disorders; fusing the human joint point features and the lip language features to obtain a fused feature, and performing text recognition on the fused feature to obtain text vocabulary information; performing speech synthesis on the text vocabulary information to obtain speech synthesis information. The apparatus executes the above method. The method and apparatus for generating speech synthesis information provided by the embodiments of the present invention can accurately and efficiently identify the speech information that customers with speech disorders want to express, and improve the business handling efficiency.

The invention belongs to the field of intelligent medical rehabilitation engineering, particularly relates to a speech recognition-based speech disorder adjuvant therapy system, and solves the problems of high pathological speech misjudgment rate, large dialect interference and single feedback in the prior art. A phoneme fault-tolerant threshold value is dynamically adjusted by constructing an adaptive speech recognition engine, regional features and pathological features are separated by using a dialect adaptation module, and three-dimensional simulation, AR real-time correction and touch graded vibration of vocal organs are realized in combination with a multi-modal feedback device; the pathological speech recognition precision is improved; and the neural remodeling efficiency is optimized.

A captioned telephone service (CTS) system provides a transcription service and a speech-to-text and text-to-speech converting service for the deaf or hard-of-hearing user with a speech disorder during a phone call between the user and the peer. The CTS system transcribes the peer's voice into text to be displayed on the user's device. The CTS system transcribes the user's voice into text based on the database storing user's spoken audios and corresponding texts, and converts the text into a clear and articulate speech to be sent to the peer's device instead of the user's voice in order to help the peer better understand what the user said. The CTS system includes a speech-to-text handler and a text-to-speech handler. The speech-to-text handler transcribes user's voice into text using the database, and the text-to-speech handler converts the text into a clear and articulate speech.

The invention provides a unified assistive technology system, the ADA Empathy Wearable Bundle, enhancing perception, communication, and self-expression for individuals with visual, auditory, or speech impairments.It integrates Empathy Glasses, Empathy Headphones, Word Articulation Guidance (WAGS), Word Articulation Response Monitoring (W.A.R.M.), and Conversational MIDI (C-MIDI), using AI-driven narrative interpretation, augmented reality, and blockchain-based security.The system delivers vivid narrative audio for blind users, multimodal AR overlays for hearing-impaired users, and real-time visual and auditory articulation guidance for speech-impaired or non-verbal users. A tone-based trust protocol ensures operational integrity, while blockchain storage preserves privacy and consent.Grounded in ethical AI, empathy, and human dignity, the system enables users to perceive, understand, and engage with the world fully, offering a holistic, compassionate, and transformative solution in assistive technology.

An electronic communication method and system are disclosed. Exemplary methods can be used by hearing or speech-impaired participants in a conference, e.g., collaboration, environment to facilitate communication between conference participants.

The invention belongs to the technical field of pronunciation disorder vocal cord vibration models, and particularly relates to a pronunciation disorder vocal cord vibration model establishing system which comprises an image acquisition module used for acquiring vocal cord vibration images by using a high-definition electronic stroboscopic laryngoscope; the image preprocessing module is used for performing enhancement, noise reduction and segmentation processing on the image acquired by the image acquisition module; and the vocal cord vibration feature extraction module is used for extracting vocal cord features according to the image processed by the image preprocessing module. The vocal cord region can be accurately identified and segmented by using a semantic segmentation algorithm based on deep learning, interference of complex backgrounds such as surrounding tissues and secretions can be effectively eliminated, the vocal cord tissues and the backgrounds can be accurately distinguished even if mucus adheres to the periphery of the vocal cord, accurate image data can be provided for subsequent feature extraction, and the vocal cord recognition accuracy is improved. And the reliability and accuracy of feature extraction are improved.

The application discloses a speech disorder evaluation method and system based on a multi-modal large model. The system comprises: acquiring corresponding multi-modal data for a target, the multi-modal data including audio signals, lip videos and tongue ultrasonic images; performing cross-modal feature extraction and semantic alignment on the multi-modal data to map the extracted modal features to a unified semantic space aligned with a large model text embedding space, obtaining a semantic vector sequence; using the semantic vector sequence as input, simulating clinical multi-level reasoning logic using the large model to obtain a preliminary evaluation result of articulation disorder, the preliminary evaluation result including severity level and disorder type; using key information in the preliminary evaluation result to set a retrieval query strategy to guide the large model to generate an evaluation report and rehabilitation suggestions. The application improves the accuracy, real-time performance and robustness of speech disorder evaluation.

A captioned telephone service system is provided for assisting users with speech disorders in communicating with peers. The system comprises a CTS server, a user device, and a user application configured with a sentence refinement unit and a text-to-speech handler. The user inputs one or more words, which are processed by the sentence refinement unit to correct errors, insert missing grammar, and generate grammatically complete sentences. A contextual adaptation module analyzes conversation and peer information to select the most relevant sentence. The user confirms, regenerates, or edits the sentence, which is then converted to natural speech by the text-to-speech handler and transmitted to the peer's device. The system further employs user and peer databases to learn from prior interactions, thereby improving accuracy and facilitating effective communication for speech-impaired individuals.

Systems and methods are provided for generating, managing, adapting, and delivering speech therapy to a user via a mobile device in an at-home or out-of-clinic setting. Users may be persons experiencing aphasia or other speech conditions. For example, prompts may be communicated to a user, and their spoken responses recorded and analyzed. Based on analysis of the responses, these systems and methods may determine a mode of response including but not limited to playback of the user's spoken response to allow the user to recursively self-assess and / or self-correct. User performance and improvement trends may be assessed, and utilized to adapt future therapy sessions.

This invention discloses a personalized synthesis and recognition enhancement method for speech disorders. The speech disorder synthesis model includes a long-range dependent feature encoding module, a non-stationary feature encoding module, and a decoding module. The input of the speech disorder synthesis model includes samples, and the output includes synthesized speech disorder speech. The samples are speech disorder text sequences. The input of the long-range dependent feature encoding module includes samples, and the output is an alignment vector z. The input of the non-stationary feature encoding module includes the alignment vector z, and the output is the final embedding representation. The input of the decoding module is the final embedding representation, and the output is the synthesized speech disorder speech. The speech disorder synthesis model of this invention improves the ability to extract personalized features of speech disorder speech, enhances speech synthesis performance, and improves the fine-grained expression of speech disorder speech features.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of UBE3A-ATS, the endogenous antisense transcript of ubiquitin protein ligase E3A (UBE3A) in a cell or subject, and in certain instances increasing the expression of paternal UBE3A and the amount of UBE3A protein in a cell or subject. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a neurogenetic disorder. Such symptoms and hallmarks include developmental delays, ataxia, speech impairment, sleep problems, seizures, and EEG abnormalities. Such neurogenetic disorders include Angelman Syndrome.

Various tools are disclosed for providing assistive or augmentative means to enhance the fluency and accuracy of persons having speech disabilities. These technologies may automatically ascertain and dynamically improve the accuracy with which automatic speech recognition (ASR) systems recognize utterances of persons having impaired speech conditions. In an embodiment, digitized audio information about a speaker's utterance is processed to determine a set of candidate words matching the utterance. From these candidate words, a set of concepts is determined using a finite state machine model. A pictogram representing each concept is identified and presented to the speaker so that the speaker may select the pictogram corresponding to the best match of his or her intended meaning associated with the utterance. An action corresponding to speaker's selection then may be performed. For example, displaying or synthesizing speech from textual information describing the selected concept.

Systems and methods are provided for generating, managing, adapting, and delivering speech therapy to a user via a mobile device in an at-home or out-of-clinic setting. Users may be persons experiencing aphasia or other speech conditions. For example, prompts may be communicated to a user, and their spoken responses recorded and analyzed. Based on analysis of the responses, these systems and methods may determine a mode of response including but not limited to playback of the user's spoken response to allow the user to recursively self-assess and / or self-correct. User performance and improvement trends may be assessed, and utilized to adapt future therapy sessions.

1. The name of the design product: voice corrector. 2. The use of the design product: for voice correction and overall treatment of functional and organic speech disorders. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: combined state reference Figure 2. 5. The bottom of the design product is not easy to see or not visible during use, omitting component 2 view from above. 6. Other circumstances that need to be explained Component Description: Component 1 is a blow mask, Component 2 is a blow training bottle, Component 3 is a blow pipe, and Component 4 is a blow auxiliary mouth.

The invention relates to a novel application of gliclazide, in particular to an application of gliclazide or pharmaceutically acceptable salt thereof in preparation of medicines for preventing and / or treating symptoms of psychosis. Wherein psychotic symptoms are generally related to schizophrenia and other diseases which may use antipsychotic drugs, including delusional, hallusion, thinking / speech disorder, agitation, obvious disorder or abnormal motor behavior (including tension), irritability and the like.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of UBE3A-ATS, the endogenous antisense transcript of ubiquitin protein ligase E3A (UBE3A) in a cell or subject, and in certain instances increasing the expression of paternal UBE3A and the amount of UBE3A protein in a cell or subject. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a neurogenetic disorder. Such symptoms and hallmarks include developmental delays, ataxia, speech impairment, sleep problems, seizures, and EEG abnormalities. Such neurogenetic disorders include Angelman Syndrome.

The invention provides a session track division method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining audio and video data recorded when a plurality of objects speak, the plurality of objects including a target object with dysarthria; processing audio data in the audio and video data to obtain text data and voice embedded data, wherein the text data carries voice composition state information; processing video data in the audio and video data to obtain video feature data including a time sequence speaking probability corresponding to each object; fusing the text data, the voice embedded data and the video feature data to obtain multi-modal fusion feature data; and carrying out speaker track division processing based on the multi-modal fusion feature data, and determining a track division result comprising the speaking information corresponding to each object. According to the method and the device, the accuracy and the anti-interference capability of multi-person session track division can be improved, and the judgment defect of a single mode in noise, overlapped voice and other scenes can be avoided.

The invention provides a group of phenotypic variables related to mental disability levels. The phenotypic variables comprise injury reasons such as traffic accidents, disturbance of consciousness, impaired degree of self-knowledge, impaired daily life ability, psychotic symptoms, mental impairment, affective disorder, impaired directional ability, speech disorder, impaired far memory, impaired social functions, the number of brain lobes with softened focuses, intentional activity enhancement or decline and impaired near memory. The invention further provides an LASSO model and a random forest model for predicting the mental disability level and a construction method of the LASSO model and the random forest model. The phenotypic variable and the related prediction model provided by the invention can be used for predicting the mental disability level, and are short in time consumption, high in efficiency and good in result accuracy and stability.

This system provides speech therapy for individuals with speech disorders, including children with disabilities, enabling them to easily and independently continue their training at home or elsewhere, without time or location constraints, even after discharge from the hospital or during outpatient visits. [Solution] The speech training support system for persons with speech disorders is configured to present a model voice converted to a slower speed using speech rate conversion technology to the person with a speech disorder who is the target of the speech training support, and then use speech rate conversion technology to convert the voice spoken by the person with a speech disorder, who imitates the model voice converted to a slower speed, back to the same speed as the model voice before conversion, and then present the high-speed converted voice to the person with a speech disorder or to the listener. By presenting the high-speed converted voice to the person with a speech disorder, training is conducted that focuses attention on the accuracy of articulation movements. The speed of the model voice converted to a slower speed is the fastest speed at which the person with a speech disorder can speak with accurate articulation without difficulty, and is between 1 / 2 and 1 times the speed of the model voice before conversion.

The invention relates to the technical field of speech recognition, in particular to a speech recognition and training method for speech disorder rehabilitation patients. The method comprises the following steps: firstly, extracting characters in voice data, and obtaining question characters pronounced by a patient and standard scores of the characters; further constructing a feature vector of each character, and classifying characters of standard pronunciation and characters of patient pronunciation; further obtaining the pronunciation disorder degree of each question character of the patient according to the character difference between the pronunciation character class cluster and the corresponding standard character class cluster and the difference of the feature vectors in combination with the standard score of the characters of the patient and the proportion of the question characters; and finally, according to the distribution of the question characters in the sentence to which the question characters belong, in combination with the pronunciation disorder degree and the occurrence frequency of the question characters, obtaining the pronunciation difficulty of each question character, accurately identifying the personalized pronunciation disorder of the patient, and providing support for related personnel to formulate a personalized training scheme and improve the intervention effect.

A system and method for providing customized interactive multimedia sessions using machine learning. A method includes obtaining a transcript for multimedia content. The transcript is analyzed using a machine learning architecture in order to generate questions and corresponding expected answers for the multimedia content. The questions are provided to a user device. Responses to the questions may be received and analyzed in order to analyze user performance. Some techniques described include methods for treating speech disorders using customized interactive multimedia sessions.