Evaluation of Noise Reduction Methods for Sentence Recognition by Mandarin-Speaking Cochlear Implant Listeners

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Abstract

Objectives:

The purpose of this study was to (1) assess the contributions of single-channel noise-reduction (NR) algorithms for improving speech intelligibility for Mandarin-speaking cochlear implant (CI) listeners and (2) examine whether different algorithms perform differently in various environmental noises.

Design:

Mandarin sentences were corrupted by three types of maskers, including speech-shaped noise, babble, and car noise, at +10, +5, or 0 dB signal to noise ratios and processed by four single-channel NR algorithms. The processed sentences were played to seven Mandarin-speaking CI patients for recognition. All patients used their own clinical speech processors in the testing.

Results:

Significant improvements in speech intelligibility were observed with most noise-suppression methods. Further analysis indicated that NR algorithms could effectively preserve the phonetic boundaries, which are critical for speech perception, and also the fundamental frequency (F0) representation was moderately improved by the NR methods.

Conclusions:

The present study demonstrated that although most single-channel NR algorithms could effectively improve speech recognition in noise for Mandarin-speaking CI listeners, these algorithms perform differently in various environmental noises, and it would be beneficial for the CI sound processor to integrate NR methods tailored to individual types of noises for the best cost and benefit tradeoff. In addition, the intelligibility improvement may be attributed to the restoration of acoustic landmark information and the improved representation of temporal F0 cues.

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