|Title||A causal deep learning framework for classifying phonemes in cochlear implants|
|Publication Type||Conference Paper|
|Year of Publication||2021|
|Authors||K Chu, L Collins, and B Mainsah|
|Conference Name||2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp)|
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.