NDLI: Exemplar-based speech enhancement for deep neural network based automatic speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Baby, D. Gemmeke, J.F. Virtanen, T. Van hamme, H.
Copyright Year	2015
Description	Author affiliation: Dept. of Signal Process., Tampere Univ. of Technol., Tampere, Finland (Virtanen, T.) \|\| Dept. ESAT, KU Leuven, Leuven, Belgium (Baby, D.; Gemmeke, J.F.; Van hamme, H.)
Abstract	Deep neural network (DNN) based acoustic modelling has been successfully used for a variety of automatic speech recognition (ASR) tasks, thanks to its ability to learn higher-level information using multiple hidden layers. This paper investigates the recently proposed exemplar-based speech enhancement technique using coupled dictionaries as a pre-processing stage for DNN-based systems. In this setting, the noisy speech is decomposed as a weighted sum of atoms in an input dictionary containing exemplars sampled from a domain of choice, and the resulting weights are applied to a coupled output dictionary containing exemplars sampled in the short-time Fourier transform (STFT) domain to directly obtain the speech and noise estimates for speech enhancement. In this work, settings using input dictionary of exemplars sampled from the STFT, Mel-integrated magnitude STFT and modulation envelope spectra are evaluated. Experiments performed on the AURORA-4 database revealed that these pre-processing stages can improve the performance of the DNN-HMM-based ASR systems with both clean and multi-condition training.
Starting Page	4485
Ending Page	4489
File Size	291973
Page Count	5
File Format	PDF
ISBN	9781467369978
DOI	10.1109/ICASSP.2015.7178819
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Speech recognition Neural networks Testing Computational modeling Speech modulation envelope deep neural networks non-negative matrix factorisation coupled dictionaries speech enhancement
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
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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