Deep Learning–Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients

Objective: We investigate the clinical effectiveness of a novel deep learning–based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI) recipients. Design: The deep learning–based NR approach used in this study consists of two modules: noise classifier (NC) and deep denoising autoencoder (DDAE), thus termed (NC + DDAE). In a series of comprehensive experiments, we conduct qualitative and quantitative analyses on the NC module and the overall NC + DDAE approach. Moreover, we evaluate the speech recognition performance of the NC + DDAE NR and classical single-microphone NR approaches for Mandarin-speaking CI recipients under different noisy conditions. The testing set contains Mandarin sentences corrupted by two types of maskers, two-talker babble noise, and a construction jackhammer noise, at 0 and 5 dB SNR levels. Two conventional NR techniques and the proposed deep learning–based approach are used to process the noisy utterances. We qualitatively compare the NR approaches by the amplitude envelope and spectrogram plots of the processed utterances. Quantitative objective measures include (1) normalized covariance measure to test the intelligibility of the utterances processed by each of the NR approaches; and (2) speech recognition tests conducted by nine Mandarin-speaking CI recipients. These nine CI recipients use their own clinical speech processors during testing. Results: The experimental results of objective evaluation and listening test indicate that under challenging listening conditions, the proposed NC + DDAE NR approach yields higher intelligibility scores than the two compared classical NR techniques, under both matched and mismatched training-testing conditions. Conclusions: When compared to the two well-known conventional NR techniques under challenging listening condition, the proposed NC + DDAE NR approach has superior noise suppression capabilities and gives less distortion for the key speech envelope information, thus, improving speech recognition more effectively for Mandarin CI recipients. The results suggest that the proposed deep learning–based NR approach can potentially be integrated into existing CI signal processors to overcome the degradation of speech perception caused by noise. ACKNOWLEDGMENTS: This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 105-2314-B-350-001, Grant MOST 106-2314-B-350-002, Grant MOST 105-2218-E-155-014-MY2, Grant MOST 106-2221-E-010-021, Grant MOST105-2218-E-001-004, and Grant CHGH 105-11. The authors have no conflicts of interest to disclose. This work was also supported by the National Natural Science Foundation of China (Grant No. 61571213). Address for correspondence: Lieber Po-Hung Li, Department of Otolaryngology, Cheng Hsin General Hospital, Taipei, Taiwan, No. 45, Cheng Hsin Street, Pai-Tou, Taipei. E-mail: lieber.chgh@gmail.com Received January 10, 2017; accepted November 1, 2017. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.

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