A Deep Denoising Autoencoder Approach to Improving the Intelligibility of Vocoded Speech in Cochlear Implant Simulation

Ying Hui Lai, Fei Chen, Syu Siang Wang, Xugang Lu, Yu Tsao*, Chin Hui Lee

*此作品的通信作者

研究成果: Article同行評審

72 引文 斯高帕斯(Scopus)

摘要

Objective: In a cochlear implant (CI) speech processor, noise reduction (NR) is a critical component for enabling CI users to attain improved speech perception under noisy conditions. Identifying an effective NR approach has long been a key topic in CI research. Method: Recently, a deep denoising autoencoder (DDAE) based NR approach was proposed and shown to be effective in restoring clean speech from noisy observations. It was also shown that DDAE could provide better performance than several existing NR methods in standardized objective evaluations. Following this success with normal speech, this paper further investigated the performance of DDAE-based NR to improve the intelligibility of envelope-based vocoded speech, which simulates speech signal processing in existing CI devices. Results: We compared the performance of speech intelligibility between DDAE-based NR and conventional single-microphone NR approaches using the noise vocoder simulation. The results of both objective evaluations and listening test showed that, under the conditions of nonstationary noise distortion, DDAE-based NR yielded higher intelligibility scores than conventional NR approaches. Conclusion and significance: This study confirmed that DDAE-based NR could potentially be integrated into a CI processor to provide more benefits to CI users under noisy conditions.

原文English
文章編號7577811
頁(從 - 到)1568-1578
頁數11
期刊IEEE Transactions on Biomedical Engineering
64
發行號7
DOIs
出版狀態Published - 7月 2017

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