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2018 | OriginalPaper | Buchkapitel

Speech Intelligibility Enhancement in Strong Mechanical Noise Based on Neural Networks

verfasst von : Feng Cheng, Xiaochen Wang, Li Gang, Weiping Tu, Jinshan Wang

Erschienen in: Advances in Multimedia Information Processing – PCM 2017

Verlag: Springer International Publishing

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Abstract

Speech intelligibility is a significant factor for successful speech communication. To enhance the intelligibility, many methods have been proposed, mainly by operating the speech signal such as increasing the amplitude or modifying the speech spectrum. However, their effects are limited when the background noise is extremely strong. In this paper, we purpose a preprocessed noise cancellation model to enhance the speech intelligibility by predicting the cancelling signal and superimposing it into the speech signal. We build a deep neural network (DNN) model to make the prediction algorithm have better accuracy. Finally, the effectiveness of the algorithm was verified by objective and subjective tests, the average of signal-to-noise ratio (SNR) improved 4.5 dB, the average of speech intelligibility index (SII) increased 5.4% and the average of comparison mean opinion score (CMOS) rose 1.16 on a variety of test cases.

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Vorheriges Kapitel PSNR Estimate for JPEG Compression
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Metadaten
Titel
Speech Intelligibility Enhancement in Strong Mechanical Noise Based on Neural Networks
verfasst von
Feng Cheng
Xiaochen Wang
Li Gang
Weiping Tu
Jinshan Wang
Copyright-Jahr
2018
Buch
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_69

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