Work

Using Automatic Speech Recognition and Speech Synthesis to Improve the Intelligibility of Cochlear Implant users in Reverberant Listening Environments

Year: 2020

Type: conference-paper

Abstract: Cochlear implant (CI) users experience substantial difficulties in understanding reverberant speech. A previous study proposed a strategy that leverages automatic speech recognition (ASR) to recognize reverberant speech and speech synthesis to translate the recognized text into anechoic speech. However, the strategy was trained and tested on the same reverberant environment, so it is unknown whether the strategy is robust to unseen environments. Thus, the current study investigated the performance of the previously proposed algorithm in multiple unseen environments. First, an ASR system was trained on anechoic and reverberant speech using different room types. Next, a speech synthesizer was trained to generate speech from the text predicted by the ASR system. Experiments were conducted in normal hearing listeners using vocoded speech, and the results showed that the strategy improved speech intelligibility in previously unseen conditions. These results suggest that the ASR-synthesis strategy can potentially benefit CI users in everyday reverberant environments.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp40776.2020.9054450

Authors: Kevin Chu, Leslie Collins, Boyla Mainsah

Institution: Duke University

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute on Deafness and Other Communication Disorders