NDLI: Combining spectral feature mapping and multi-channel model-based source separation for noise-robust automatic speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Bagchi, Deblin Mandel, Michael I. Wang, Zhongqiu He, Yanzhang Plummer, Andrew Fosler-Lussier, Eric
Copyright Year	2015
Description	Author affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA (Bagchi, Deblin; Mandel, Michael I.; Wang, Zhongqiu; He, Yanzhang; Plummer, Andrew; Fosler-Lussier, Eric)
Abstract	Automatic Speech Recognition systems suffer from severe performance degradation in the presence of myriad complicating factors such as noise, reverberation, multiple speech sources, multiple recording devices, etc. Previous challenges have sparked much innovation when it comes to designing systems capable of handling these complications. In this spirit, the CHiME-3 challenge presents system builders with the task of recognizing speech in a real-world noisy setting wherein speakers talk to an array of 6 microphones in a tablet. In order to address these issues, we explore the effectiveness of first applying a model-based source separation mask to the output of a beamformer that combines the source signals recorded by each microphone, followed by a DNN-based front end spectral mapper that predicts clean filterbank features. The source separation algorithm MESSL (Model-based EM Source Separation and Localization) has been extended from two channels to multiple channels in order to meet the demands of the challenge. We report on interactions between the two systems, cross-cut by the use of a robust beamforming algorithm called BeamformIt. Evaluations of different system settings reveal that combining MESSL and the spectral mapper together on the baseline beamformer algorithm boosts the performance substantially.
Starting Page	496
Ending Page	503
File Size	618180
Page Count	8
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404836
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	Microphones Source separation Speech Noise measurement Array signal processing Robustness Time-frequency analysis Beamforming Robust Automatic Speech Recognition Deep Neural Networks Spectral Feature Mapping Multi-channel Model-based Source Separation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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