Paralinguistic speech processing is important in addressing many issues, such as sentiment
and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable …

Understanding the computational algorithm that gives rise to human language is a shared
endeavor among neuroscience, linguistics, and machine learning. We propose a conceptual …

Modern language models excel at integrating across long temporal scales needed to
encode linguistic meaning and show non-trivial similarities to biological neural systems …

Timescales of neural activity are diverse across and within brain areas, and experimental
observations suggest that neural timescales reflect information in dynamic environments …

Cochlear implants (CIs) are arguably the most successful neural implant, having restored
hearing to over one million people worldwide. While CI research has focused on modeling …

A speech recognition system aims to translate audio input into a string of words. Deep
Speech is an end-to-end programmed communication acknowledgment engine that has …

Arabic raw audio datasets were initially gathered to produce a corresponding signal
spectrum, which was further used to extract the Mel-Frequency Cepstral Coefficients …

Sound structures such as phonemes and words have highly variable durations. Thus, there
is a fundamental difference between integrating across absolute time (eg, 100 ms) vs. sound …

The human brain's ability to transform acoustic speech signals into rich linguistic
representations has inspired advancements in automatic speech recognition (ASR) systems …

The human brain's ability to understand complex natural stimuli, such as speech, music,
language, and visual scenes and objects, is remarkable for its accuracy, efficiency, and …