Leveraging automatic speech recognition in cochlear implants for improved speech intelligibility under reverberation

Abstract

Despite recent advancements in digital signal processing technology for cochlear implant (CI) devices, there still remains a significant gap between speech identification performance of CI users in reverberation compared to that in anechoic quiet conditions. Alternatively, automatic speech recognition (ASR) systems have seen significant improvements in recent years resulting in robust speech recognition in a variety of adverse environments, including reverberation. In this study, we exploit advancements seen in ASR technology for alternative formulated solutions to benefit CI users. Specifically, an ASR system is developed using multicondition training on speech data with different reverberation characteristics (e.g., T 60 values), resulting in low word error rates (WER) in reverberant conditions. A speech synthesizer is then utilized to generate speech waveforms from the output of the ASR system, from which the synthesized speech is presented to CI listeners. The effectiveness of this hybrid recognition-synthesis CI strategy is evaluated under moderate to highly reverberant conditions (i.e., T 60 = 0.3, 0.6, 0.8, and 1.0s) using speech material extracted from the TIMIT corpus. Experimental results confirm the effectiveness of multi-condition training on performance of the ASR system in reverberation, which consequently results in substantial speech intelligibility gains for CI users in reverberant environments.