87 results about "Speech sounds" patented technology

The invention provides a practical training method and system based on multi-modal Internet of Things perception and virtual-real symbiosis, and relates to the technical field of virtual-real symbiosis, and the method comprises the steps: collecting the multi-modal perception data of a practical training environment through a plurality of heterogeneous Internet of Things devices arranged in the practical training environment; constructing a three-dimensional digital twinborn model of the practical training environment according to the category and position information of the object in the practical training environment, the voice control instruction and the comprehensive environment parameters; the method comprises the following steps: setting training items and evaluation indexes in a virtual-real symbiotic training space, receiving a training item selection instruction input by training personnel, and loading a corresponding training item according to the selection instruction, and sending a control instruction of the practical training project to a virtual equipment model in the three-dimensional digital twinborn model and Internet of Things equipment mapped with the virtual equipment model in the practical training environment for practical training, and evaluating the practical training process and the practical training result of the practical training project according to the evaluation index after the practical training project is finished. And a training evaluation report is generated and fed back to the training management system.

The invention relates to the technical field of rehabilitation training, in particular to a multi-logic interaction system for speech disorder rehabilitation training, which comprises a voice accuracy analysis module, a voice mouth shape comparison module, a training item distribution module, a training difficulty adjustment module and a review interval optimization module. By capturing and analyzing the pronunciation information of the user in real time, pronunciation errors can be accurately recognized and corrected, the pertinence and efficiency of voice training are enhanced, a patient can visualize the pronunciation errors by combining voice data with a mouth shape image, and meanwhile, by comparing standard pronunciation images, the pronunciation errors can be accurately recognized and corrected. According to the method, integration of voice and visual information is enhanced, each patient can obtain a rehabilitation scheme meeting target requirements and ability through intelligent distribution of training content and time and dynamic adjustment of training difficulty, training distribution and difficulty adjustment are optimized, a user can train in a proper rhythm, and the training efficiency is improved. And the individuation level and the overall effect of speech disorder rehabilitation training are improved.

The invention relates to the technical field of voice analysis, and particularly discloses a vocal music training method and system based on artificial intelligence, and the method comprises the steps: collecting and analyzing the body posture parameters of a target vocal music training person, carrying out the preprocessing, detecting the posture abnormality, forming a first abnormal training set, capturing and analyzing the pronunciation feature parameters, and recognizing the pronunciation abnormality, thereby obtaining a vocal music training result; and integrating the first abnormal training set and the second abnormal training set to generate a total abnormal parameter training set, matching a correction set, and visually displaying the body posture abnormal degree and the abnormal reason and the pronunciation abnormal degree and the reason, so that the target vocal music training personnel can visually see own posture and pronunciation problems, and self-adjustment and correction are facilitated.

An estimation method includes: obtaining a first voice feature group of a plurality of persons who speak a first language; obtaining a second voice feature group of a plurality of persons who speak a second language; obtaining a voice feature of a subject; correcting the voice feature of the subject according to a relationship between the first voice feature group and the second voice feature group; estimating, from the voice feature of the subject that has been corrected, an oral function or a cognitive function of the subject by using an estimation process for an oral function or a cognitive function based on the second language; and outputting a result of estimation of the oral function or the cognitive function of the subject.

The invention relates to the technical field of English pronunciation learning, and discloses an AI (artificial intelligence) English learning system capable of correcting pronunciation. The system comprises a multi-source voice acquisition layer, an acoustic feature analysis layer, a pronunciation quality evaluation layer, a correction decision generation layer and a biological feedback execution layer. The multi-source voice acquisition layer captures a multi-channel sound wave signal and a maxillofacial electromyographic signal in real time when a user pronounces, and generates an original pronunciation data stream; the acoustic feature analysis layer performs time-frequency domain conversion on original data, separates multi-scale acoustic features and constructs a three-dimensional feature tensor; the pronunciation quality evaluation layer compares the standard template library to output scores and phoneme offset vectors; the correction decision generation layer constructs a pronunciation error topological graph and generates a correction instruction set containing tongue position, airflow and vocal cord parameters; and the biological feedback execution layer synchronously monitors the physiological response and updates the weight of the template library. According to the system, accurate acquisition, analysis, evaluation, correction and dynamic optimization of pronunciation learning are realized, and the English pronunciation learning effect is improved.

The embodiment of the invention provides a sound production device based on ultrasonic recognition, and the device comprises a transmitting type ultrasonic transducer which is used for transmitting an ultrasonic signal; the emission type ultrasonic transducer is installed in the oral cavity or the nasal cavity of a user or installed on the neck or the lower jaw of the user. The receiving type ultrasonic transducer is used for collecting ultrasonic signals; the analog-to-digital converter is used for converting the ultrasonic signal into a digital signal; the coding chip is used for coding the digital signal to obtain a coded signal; the processing module is used for performing voice recognition on the coded signal based on a voice recognition algorithm to obtain a synthesized voice signal; and the voice emitting module is used for driving a loudspeaker to emit a sound based on the synthesized voice signal. Therefore, the sound made by the user is more natural and rich in emotion, the communication efficiency of the user is improved, and the experience feeling of the user is improved.

The invention discloses an intelligent exoskeleton bone auxiliary conduction hearing aid system capable of being remotely controlled and having an electroencephalogram control function, and relates to the technical field of intelligent rehabilitation robots and hearing aid devices. The wearable mechanical part comprises a flexible actuator, an elastic fabric base material, an inertial measurement unit IMU, a plantar pressure sensor array, a surface myoelectricity EMG sensor and an electroencephalogram EEG sensor. And the information processing communication part comprises a central controller, a bone conduction hearing aid module, a multi-mode sensor, an embedded SoC, a DSP / AI accelerator, a 4G / 5G / Wi-Fi module and a power management unit. The flexible exoskeleton walking aid system and the bone conduction hearing aid module are integrated, the electroencephalogram EEG sensor is additionally arranged to achieve the electroencephalogram control function, and cooperation of the three functions of movement assistance, auditory perception and neural intention recognition is achieved. A user can directly control the exoskeleton action mode through electroencephalogram signals, meanwhile, intelligent assistance of lower limb joints is obtained in the walking process, and environment sounds and voice prompts are clearly received in a bone conduction mode. According to the system, the perception ability, the control flexibility and the social participation degree of old people or rehabilitation patients who are inconvenient to move and accompanied with hearing impairment in a complex environment are remarkably improved, and more natural and convenient man-machine interaction experience is provided for users.

A method for generating speech includes uploading a reference set of features that were extracted from sensed movements of one or more target regions of skin on faces of one or more reference human subjects in response to words articulated by the subjects and without contacting the one or more target regions. A test set of features is extracted a from the sensed movements of at least one of the target regions of skin on a face of a test subject in response to words articulated silently by the test subject and without contacting the one or more target regions. The extracted test set of features is compared to the reference set of features, and, based on the comparison, a speech output is generated, that includes the articulated words of the test subject.

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).

Voice-interaction massage device for massaging at least one penis, comprising the following: a housing comprising a first massage chamber and a second massage chamber, each of the first massage chamber and the second massage chamber being configured to accommodate at least part of the penis; a massage module incorporated in the housing, wherein the massage module is configured to massage at least a part of the at least one penis that is inserted into at least one of the two massage chambers; a loudspeaker; and a first sensor configured to detect movements of the penis in the first massage chamber; a control unit that is electrically connected to the speaker and the first sensor; the control unit, as soon as the first sensor detects movements of the penis in the first massage chamber, activates the speaker to output an initial voice message.

The application provides a model training method and a bipolar disorder personalized treatment dialogue generation method and device, belonging to the technical field of deep learning, aiming at the problems that the current diagnosis accuracy of bipolar mood disorder is not high and the matching of treatment dialogue with patients is poor, the model training can be performed on a multi-modal language model according to a voice signal with significant characteristics of bipolar mood disorder, and a target classification model for bipolar mood disorder classification is obtained. Since the voice signal contains characteristics such as semantics, tone, pronunciation rhythm and other characteristics directly related to bipolar mood disorder expressed by the patient, the target classification model can be used for accurate classification of bipolar mood disorder, and when the target classification model is used for classification of bipolar mood disorder, the excessive dependence on the subjective diagnosis of doctors can be avoided, and the accuracy of the classification prediction of bipolar mood disorder is effectively improved.

The present invention discloses a data processing method and system for multimodal facial motion point data and vocal cord motion data. The method includes providing text, collecting continuous facial images or videos and laryngeal vibration data of a normal person speaking, preprocessing the data, extracting temporal and spatial features, establishing a facial and neck motion model for Chinese pronunciation, and having a deaf-mute person imitate the pronunciation according to the model and obtain feedback. The system includes a depth camera, a laryngeal vibration sensor, and a microphone. By comprehensively utilizing multimodal data, it provides instant feedback to the deaf-mute, lowers the learning threshold, and improves communication efficiency. It is applicable to deaf-mute groups worldwide. This invention promotes speech vocalization training and has broad application prospects and social significance.

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.

A machine learning based patient voice monitoring and analysis system can reduce the need for patient hospitalization by early detection and treatment of health conditions such as acute decompensated heart failure.

The invention relates to the technical field of children language rehabilitation training, and provides a children language rehabilitation training system based on voice and mouth shape recognition, which comprises a mouth shape simulation frame, a voice and mouth shape recognition module, a voice and mouth shape recognition module and a voice and mouth shape recognition module, an external camera is used for tracking and simulating the lips and the face of the training object in a model animation mode; and the tracking and capturing module is used for collecting face data of the training object and feeding back the face data to the model of the simulation mouth shape frame so as to fit the face of the training object and improve the training conformity. According to the invention, specific facial features and mouth shapes of each child can be individually fitted, customized rehabilitation training is provided, the mouth shapes of the children can be better fitted, more accurate pronunciation mouth shapes can be guided to be made, meanwhile, guide animations and roles which attract the attention of the children can be generated, the participation degree of the children is improved, the rehabilitation training is more interactive and interesting, and the interestingness of the children is improved. And the language learning interest and effect of the children can be improved.

The invention discloses a global nonlinear dynamics vocal cord model implementation method and system, and relates to the technical field of nonlinear dynamics and biomechanics, and the method comprises the steps: introducing a vocal cord tension coefficient, and constructing a left-right asymmetric coefficient; introducing a Brown random jitter factor to correct the left-right asymmetric coefficient so as to construct a global nonlinear dynamic vocal cord model; and introducing a river horse optimization module to realize dynamic updating of vocal cord model parameters. According to the method, the left and right asymmetric coefficients and the vocal cord tension coefficient are introduced, so that the dynamic change of the vocal cord structure can be represented more meticulously, particularly, the difference of the mass, the elastic coefficient and the damping coefficient of the left and right sides of the vocal cord is accurately described, and the dynamic tracking of the change of the vocal cord structure is realized; through the construction of a global nonlinear model, the local change of the vocal cord is combined with the overall dynamic behavior, the dynamic characteristics of the vocal cord under different sounding states or pathological conditions are reflected, and a finer modeling basis is provided for vocal cord disease diagnosis and voice research.

The invention discloses a digital human generation system and method based on a language model, and relates to the technical field of artificial intelligence. A complete digital human pronunciation modeling path from voice signal acquisition, formant frequency extraction, deviation analysis and pronunciation stability evaluation to motion compensation control and three-dimensional animation fusion is realized. Compared with the existing mode of driving the digital human pattern only based on energy envelope or mouth shape classification, the method has the advantages that the multi-frame formant frequency of the user voice is extracted and processed, so that the digital human can generate dynamic lingual surface and tongue tip actions according to the sound channel change in the real pronunciation process of the user; therefore, the physical correspondence and linguistic consistency of the pronunciation actions of the digital human are improved. According to the method, the direct mapping between the pronunciation characteristics and the three-dimensional tongue actions is realized, and the authenticity of the pronunciation animation of the digital human and the guidance in a teaching scene are remarkably improved.

An object of the system according to the embodiment is to analyze a cry or an action of a pet and accurately convert an intention or an emotion into a human language.SOLUTION: A system includes a collection unit, an analysis unit, an estimation unit, and a conversion unit. The collection unit collects a cry or an action. The analysis unit analyzes the data collected by the collection unit. The estimation unit estimates an intention or a feeling on the basis of an analysis result obtained by the analysis unit. The conversion unit converts the intention or the feeling estimated by the estimation unit into human language.SELECTED DRAWING: Figure 1

This utility model discloses a voice tremor nursing teaching model in the field of nursing teaching aids. It includes a three-dimensional humanoid model with a left lung model and a right lung model positioned at the chest area. The left lung model is covered with a silicone membrane, forming a pleural cavity model. A catheter is located at the top of the left lung model, with one end extending into the pleural cavity model. Air or liquid is injected into the pleural cavity through the catheter to simulate pneumothorax or pleural effusion. The right lung model includes a detachable and replaceable lung cavity model, lung tumor model, and bronchial obstruction model. A bronchial model is located between the left and right lung models, with a microphone connected to the top of the bronchial model. The two forked ends of the bronchial model at the bottom are connected to the left and right lung models, respectively. This device can replace different models to simulate pathological states, allowing students to intuitively experience the differences in voice tremor under different pathological conditions, improving teaching effectiveness and students' clinical skills training level.

In one aspect, the disclosure relates to a smart pseudo-palate for use in a Smart Electropalatograph (EPG) for Linguistic and Medical Applications (SELMA) system. In one aspect, the pseudo-palate is constructed from a thin, flexible polymer membrane and having an embedded electrode array. The pseudo-palate is configured to detect tongue contacts during speech while causing minimal disturbance or interference with speech motion. The disclosed pseudo-palate in the SELMA system is integrated with a microcontroller, wireless electronic module, and external readout app. The disclosure, in another aspect, relates to integration of the pseudo-palate with a smart sports / health mouth guard containing a series of sensors for monitoring head impacts, body temperature, and heart rate. The SELMA system is capable of automated detection of neurological conditions and brain injury including, but not limited to, concussion, and neurological movement disorders, using acoustic, articulatory, and other biosignals from the device using deep data analysis.

This invention discloses a humanized voice intervention method for inhalation errors in nasal and oral aerosol delivery devices, aiming to address the lack of humanization in existing voice intervention methods, which leads to patient misunderstanding and resistance. The method includes: establishing a patient-doctor corpus by combining expert experience, literature, and guidelines; fine-tuning a language parsing model to obtain an error-sensitive parsing model; using the error-sensitive parsing model to obtain a total intervention feature library and an urgency level sub-library; establishing a two-level query mechanism for patient inhalation errors to query intervention text in the urgency level sub-library; and fine-tuning the voice generation model based on the aforementioned patient-doctor corpus, incorporating new tokens, and evolving through prompting learning into a patient-friendly intervention voice generation model capable of generating intervention voices that are appropriate for the urgency level. The intervention voices generated by this invention can form a humanized voice intervention that approximates the tone of clinical medical staff, and can adapt the tone according to the urgency level of the error, effectively improving the patient's understanding and acceptance of the intervention suggestions.

The invention provides an artificial intelligence-based ear-nose-throat specialized clinical teaching effect evaluation system. The system comprises a natural language processing module, a computer vision analysis module, a virtual patient evaluation module, an otoscope image acquisition device, an image recognition module, an intelligent scoring and feedback module, a literature retrieval scoring module, a data management module and a visual presentation module. According to the system, multi-modal data of students in the processes of voice answering, skill operation, virtual inquiry, otoscope image recognition and literature retrieval are collected, automatic analysis and scoring are carried out, an ability evaluation report and personalized feedback are generated, standardization and intelligence of teaching evaluation are achieved, and the teaching efficiency is improved. The comprehensive ability evaluation device is suitable for comprehensive ability evaluation in the ear-nose-throat specialized medical teaching process. The system has the advantages of objective scoring, efficient processing, visual feedback and the like.

The present invention relates to the technical field of exhaled gas sampling, and discloses an exhaled gas phased collection device, including a host system and a power supply module; the host system includes a gas drying module, an information interaction module, a gas phased collection module, a main control module and a data processing module. The information interaction module is used to select the exhaled gas collection mode and the interaction of voice and display information. The main control module controls the gas phased collection module according to the selected exhaled gas collection mode to simultaneously collect single or multiple selected exhaled gases in the ambient gas, dead space gas, and alveolar gas in phases, detect the gas collection status, and the installation status of the disposable mouthpiece and air bag during the exhaled gas collection process, and the inflation status of the air bag at the gas collection port. The above-mentioned exhaled gas phased collection device is intended to divide the gas exhaled by the human body into different stages for collection, provide technical means for collecting the corresponding stage gas for exhaled gas detection of different diseases, and improve and promote the development of disease exhaled gas detection technology.

The invention provides an easily-confused emotion feature extraction method based on adaptive wavelet transform, and aims to improve the recognition performance of easily-confused emotions. According to the method, learnable parameters beta, gamma and a are introduced into a wavelet basis function, the time-frequency form of the wavelet basis function is dynamically adjusted, multi-scale time-frequency decomposition is carried out on input voice signals, two-dimensional tensor feature representation is obtained, and extracted features reserve time local changes of the voice signals and differences of different frequency bands in emotion distinguishing. Meanwhile, a projection gradient descent method is adopted to carry out constraint optimization on parameters, the stability and energy convergence of a primary function are ensured, and a gradient return updating mechanism is utilized to enable wavelet parameters to be adaptively optimized in the training process. According to the method, emotional characteristics with higher discrimination and robustness can be extracted, and the accuracy and generalization ability of emotion recognition are improved.

The invention relates to the technical field of rehabilitation assessment, in particular to a cerebral apoplexy rehabilitation assessment method and system based on voice multi-task learning, and the method comprises the following steps: carrying out the psychological emotion recognition of assessment voice data, and obtaining a cerebral apoplexy rehabilitation subjective assessment task used for perceiving the subjective rehabilitation feeling of an assessment object; performing physiological function recognition on the evaluation voice data to obtain a cerebral apoplexy rehabilitation objective evaluation task for perceiving the objective rehabilitation state of the evaluation object; and performing weight adaptive combination on the stroke rehabilitation subjective evaluation task and the stroke rehabilitation objective evaluation task by using a multi-task learning mechanism to obtain a stroke rehabilitation evaluation model. According to the method, objective function states and subjective emotional feelings fed back from the voices of the patient can be taken into consideration during rehabilitation evaluation, pain and discomfort of the stroke patient can be perceived during functional evaluation, the individual accuracy of stroke rehabilitation evaluation is improved, and the user experience is enhanced.

The invention discloses a voice anomaly detection method for pre-driving period screening of Parkinson's disease, which is mainly used for performing non-invasive pre-driving period screening of the Parkinson's disease on voice information of a subject, analyzing by using daily voice, and discovering early signs of the Parkinson's disease without invasive examination such as blood drawing, imaging and the like. The method provides a new way for large-scale population screening, reduces the detection cost, is good in patient compliance, is high in result interpretation, is combined with a knowledge graph or feature importance analysis, provides a basis for an anomaly detection result, enables a clinician to understand the correlation between voice anomaly and Parkinson pathology, and improves the detection accuracy. And the credibility of the AI result is improved.

The invention, which relates to a filter element (1) for respiratory gas conditioning with a speech function, is based on the objective of providing a filter element (1) for use with a tracheotomized or laryngectomized person, wherein the filter element (1) is robust, easy to operate, and inexpensive to manufacture. This objective is achieved by the housing (2) comprising a first housing part (5) and a second housing part (6) connected to the first housing part (5), the first opening (3) being arranged in the second housing part (6), the first housing part (5) being a single piece comprising an outer ring (7), a central upper part (8), and elastically deformable webs (9) arranged between the outer ring (7) and the upper part (8).

The invention discloses a multifunctional swallowing training and sound production auxiliary exercising device, and belongs to the technical field of sound production training devices. Comprising an arc-shaped plate fixed to the lower jaw of a user, a supporting plate fixed to the chest of the user, a fixing column connecting the arc-shaped plate and the supporting plate and a rear neck adjusting belt, and a pickup, a displayer, a display lamp and a control terminal are integrated. The sound pick-up collects voice signals, the displayer displays volume, and the display lamp feeds back sound production quality through the control terminal. A piezoelectric sensor is embedded in the supporting plate to monitor the thoracic expansion amplitude; when chest breathing is detected, the clavicle heating module is linked to heat up, phrenic nerve reflex is activated through thermal stimulation to guide abdominal breathing, vocal cord closure is improved, and the risk of aspiration by mistake is reduced. The equipment is integrated with a rotating assembly to realize multi-angle adjustment and automatic switching of the display; a multi-frequency vibration module and a temperature control heating module are arranged in the arc-shaped plate, and targeted muscle stimulation is provided. Through mechanical linkage and multi-mode feedback, the breathing-sounding-swallowing cooperative training efficiency is optimized, and the user suitability and the rehabilitation effect are improved.