AIMC Topic: Speech Perception
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Single-microphone deep envelope separation based auditory attention decoding for competing speech and music.
Journal of neural engineering
May 7, 2025
In this study, we introduce an end-to-end single microphone deep learning system for source separation and auditory attention decoding (AAD) in a competing speech and music setup. Deep source separation is applied directly on the envelope of the obse...
Automatic development of speech-in-noise hearing tests using machine learning.
Scientific reports
Apr 15, 2025
Understanding speech in noisy environments is a primary challenge for individuals with hearing loss, affecting daily communication and quality of life. Traditional speech-in-noise tests are essential for screening and diagnosing hearing loss but are ...
Natural language processing models reveal neural dynamics of human conversation.
Nature communications
Apr 9, 2025
Through conversation, humans engage in a complex process of alternating speech production and comprehension to communicate. The neural mechanisms that underlie these complementary processes through which information is precisely conveyed by language,...
Enhancing visual speech perception through deep automatic lipreading: A systematic review.
Computers in biology and medicine
Mar 28, 2025
Communication involves exchanging information between individuals or groups through various media sources. However, limitations such as hearing loss can make it difficult for some individuals to understand the information delivered during speech comm...
A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations.
Nature human behaviour
Mar 7, 2025
This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals acr...
Incremental accumulation of linguistic context in artificial and biological neural networks.
Nature communications
Jan 18, 2025
Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we s...
Endpoint-aware audio-visual speech enhancement utilizing dynamic weight modulation based on SNR estimation.
Neural networks : the official journal of the International Neural Network Society
Jan 11, 2025
Integrating visual features has been proven effective for deep learning-based speech quality enhancement, particularly in highly noisy environments. However, these models may suffer from redundant information, resulting in performance deterioration w...
Unraveling the Differential Efficiency of Dorsal and Ventral Pathways in Visual Semantic Decoding.
International journal of neural systems
Jan 10, 2025
Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual...
Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.
PLoS computational biology
Nov 11, 2024
To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) ...
Speech recognition using an english multimodal corpus with integrated image and depth information.
Scientific reports
Nov 6, 2024
Traditional English corpora mainly collect information from a single modality, but lack information from multimodal information, resulting in low quality of corpus information and certain problems with recognition accuracy. To solve the above problem...
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