A causal deep learning framework for classifying phonemes in cochlear implants

TitleA causal deep learning framework for classifying phonemes in cochlear implants
Publication TypeConference Paper
Year of Publication2021
AuthorsK Chu, L Collins, and B Mainsah
Conference Name2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp)
Date Published01/2021
Abstract

Speech intelligibility in cochlear implant (CI) users degrades considerably in listening environments with reverberation and noise. Previous research in automatic speech recognition (ASR) has shown that phoneme-based speech enhancement algorithms improve ASR system performance in reverberant environments as compared to a global model. However, phoneme-specific speech processing has not yet been implemented in CIs. In this paper, we propose a causal deep learning framework for classifying phonemes using features extracted at the time-frequency resolution of a CI processor. We trained and tested long short-term memory networks to classify phonemes and manner of articulation in anechoic and reverberant conditions. The results showed that CI-inspired features provide slightly higher levels of performance than traditional ASR features. To the best of our knowledge, this study is the first to provide a classification framework with the potential to categorize phonetic units in real-time in a CI.

DOI10.1109/ICASSP39728.2021.9413986