A Causal Deep Learning Framework for Classifying Phonemes in Cochlear Implants
| Autor: | Kevin M. Chu, Leslie M. Collins, Boyla O. Mainsah |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Reverberation
Computer science business.industry Speech recognition Deep learning medicine.medical_treatment Phonetics Speech processing ComputingMethodologies_ARTIFICIALINTELLIGENCE Manner of articulation Article Speech enhancement Noise ComputingMethodologies_PATTERNRECOGNITION Cochlear implant medicine Artificial intelligence business |
| Zdroj: | ICASSP Proc IEEE Int Conf Acoust Speech Signal Process |
| DOI: | 10.1109/icassp39728.2021.9413986 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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