Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods stru...
This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-n...
The Journal of the Acoustical Society of America
38884525
For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environments is often challenging. Deep-learning algorithms can potentially mitigate these difficulties by enhancing speech in everyday listening environments. This ...
Cochlear implants (CIs) do not offer the same level of effectiveness in noisy environments as in quiet settings. Current single-microphone noise reduction algorithms in hearing aids and CIs only remove predictable, stationary noise, and are ineffecti...
The Journal of the Acoustical Society of America
39082692
Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not per...
Electrophysiological research has been widely utilized to study brain responses to acoustic stimuli. The frequency-following response (FFR), a non-invasive reflection of how the brain encodes acoustic stimuli, is a particularly propitious electrophys...
Adults struggle to learn non-native speech categories in many experimental settings (Goto, Neuropsychologia, 9(3), 317-323 1971), but learn efficiently in a video game paradigm where non-native speech sounds have functional significance (Lim & Holt, ...
The Journal of the Acoustical Society of America
39248559
A speech intelligibility (SI) prediction model is proposed that includes an auditory preprocessing component based on the physiological anatomy and activity of the human ear, a hierarchical spiking neural network, and a decision back-end processing b...
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) ...
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...