NDLI: Multi-channel speech processing architectures for noise robust speech recognition: 3rd CHiME challenge results

Content Provider	IEEE Xplore Digital Library
Author	Pfeifenberger, Lukas Schrank, Tobias Zohrer, Matthias Hagmuller, Martin Pernkopf, Franz
Copyright Year	2015
Description	Author affiliation: Signal Processing and Speech Communication Laboratory, Graz University of Technology, Graz, Austria (Pfeifenberger, Lukas; Schrank, Tobias; Zohrer, Matthias; Hagmuller, Martin; Pernkopf, Franz)
Abstract	Recognizing speech under noisy condition is an ill-posed problem. The CHiME 3 challenge targets robust speech recognition in realistic environments such as street, bus, caffee and pedestrian areas. We study variants of beamformers used for pre-processing multi-channel speech recordings. In particular, we investigate three variants of generalized side-lobe canceller (GSC) beamformers, i.e. GSC with sparse blocking matrix (BM), GSC with adaptive BM (ABM), and GSC with minimum variance distortionless response (MVDR) and ABM. Furthermore, we apply several post-filters to further enhance the speech signal. We introduce MaxPower postfilters and deep neural postfilters (DPFs). DPFs outperformed our baseline systems significantly when measuring the overall perceptual score (OPS) and the perceptual evaluation of speech quality (PESQ). In particular DPFs achieved an average relative improvement of 17.54% OPS points and 18.28% in PESQ, when compared to the CHiME 3 baseline. DPFs also achieved the best WER when combined with an ASR engine on simulated development and evaluation data, i.e. 8.98% and 10.82% WER. The proposed MaxPower beamformer achieved the best overall WER on CHiME 3 real development and evaluation data, i.e. 14.23% and 22.12%, respectively.
Starting Page	452
Ending Page	459
File Size	428712
Page Count	8
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Speech recognition Microphones Artificial neural networks Speech enhancement Array signal processing automatic speech recognition multi-channel speech processing deep postfilter
Content Type	Text
Resource Type	Article

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