5 results about "Speech disturbances" patented technology

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 application discloses a speech data processing method and system based on a large language model, relates to the technical field of speech data processing, and comprises the following steps: performing multi-channel feature decomposition on a received original speech signal, and constructing an acoustic state representation tensor; constructing a semantic candidate distribution space, generating multiple sets of semantic hypothesis vectors, and constructing a semantic evolution path graph; generating a semantic uncertainty function representing semantic ambiguity and speech disturbance sensitivity; dynamically constructing a reasoning depth control parameter and inputting the same to a multi-layer reasoning path scheduling unit of the large language model, constructing an intention structure vector, and mapping the intention structure vector into a structured semantic output. The technical problems that in the prior art, under a complex acoustic environment, it is difficult to accurately and effectively separate acoustic features, leading to low speech understanding accuracy of high ambiguity, and lacking dynamic reasoning ability to cope with semantic uncertainty risks are solved, and the technical effects of improving semantic understanding precision, ambiguity resolution ability of speech interaction, and reducing business misjudgment rate and risk are achieved.

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.

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.

This invention provides a multimodal speech ability assessment method and apparatus based on generative artificial intelligence. It efficiently processes multimodal data through an asynchronous parallel mechanism and employs specialized techniques for in-depth analysis of different modal characteristics: for speech manuscripts, it utilizes structured cue words to guide a large language model, achieving multi-dimensional and standardized semantic assessment of text quality; for presentation slides, it applies vector retrieval-enhanced generation technology to accurately locate core content and assess its structure and design; for audio data, it calls professional interfaces for streaming analysis to obtain overall dimensional scores and word-level diagnostics that pinpoint specific word pronunciation problems; for video data, it identifies the speaker's emotional state through a multimodal model that integrates spatiotemporal, audio, and text features. The standardized integration and unified display of the assessment results from each modality achieves a comprehensive and in-depth assessment of speech ability, significantly improving assessment efficiency and practical teaching value.