Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement

Ming Ji, Daniel Crookes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
190 Downloads (Pure)

Abstract

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
DOIs
Publication statusPublished - 19 May 2016
EventThe 41st IEEE International Conference on Acoustics, Speech and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

ConferenceThe 41st IEEE International Conference on Acoustics, Speech and Signal Processing
CountryChina
CityShanghai
Period20/03/201625/03/2016

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  • Cite this

    Ji, M., & Crookes, D. (2016). Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement. In Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.2016.7472811