Contribution of noise reduction pre-processing and microphone directionality strategies in the speech recognition in noise in adult cochlear implant users
- 18 September 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in European Archives Of Oto-Rhino-Laryngology
- Vol. 278 (8), 2823-2828
- https://doi.org/10.1007/s00405-020-06372-2
Abstract
Purpose Refinement currently offered in new sound processors may improve noise listening capability reducing constant background noise and enhancing listening in challenging signal-to-noise conditions. This study aimed to identify whether the new version of speech processor preprocessing strategy contributes to speech recognition in background noise compared to the previous generation processor. Methods This was a multicentric prospective cross-sectional study. Post-lingually deaf adult patients, with at least 1 year of device use and speech recognition scores above 60% on HINT sentences in quiet were invited. Speech recognition performance in quiet and in noise with sound processors with previous and recent technologies was assessed under four conditions with speech coming from the front: (a) quiet (b) fixed noise coming from the front, (c) fixed noise coming from the back, and (d) adaptive noise ratios with noise coming from the front. Results Forty-seven cochlear implant users were included. No significant difference was found in quiet condition. Performance with the new processor was statistically better than the previous sound processor in all three noisy conditions (p < 0.05). With fixed noise coming from the back condition, speech recognition was 62.9% with the previous technology and 73.5% on the new one (p < 0.05). The mean speech recognition in noise was also statistically higher, with 5.8 dB and 7.1 dB for the newer and older technologies (p < 0.05), respectively. Conclusion New technology has shown to provide benefits regarding speech recognition in noise. In addition, the new background noise reduction technology, has shown to be effective and improves speech recognition in situations of more intense noise coming from behind.Keywords
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