NDLI: Spectro-temporal features for noise-robust speech recognition using power-law nonlinearity and power-bias subtraction

Content Provider	IEEE Xplore Digital Library
Author	Shuo-Yiin Chang Meyer, B.T. Morgan, N.
Copyright Year	2013
Description	Author affiliation: Int. Comput. Sci. Inst., Berkeley, CA, USA (Shuo-Yiin Chang; Meyer, B.T.; Morgan, N.)
Abstract	Previous work has demonstrated that spectro-temporal Gabor features reduced word error rates for automatic speech recognition under noisy conditions. However, the features based on mel spectra were easily corrupted in the presence of noise or channel distortion. We have exploited an algorithm for power normalized cepstral coefficients (PNCCs) to generate a more robust spectro-temporal representation. We refer to it as power normalized spectrum (PNS), and to the corresponding output processed by Gabor filters and MLP nonlinear weighting as PNS-Gabor. We show that the proposed feature outperforms state-of-the-art noise-robust features, ETSI-AFE and PNCC for both Aurora2 and a noisy version of the Wall Street Jounal (WSJ) corpus. A comparison of the individual processing steps of mel spectra and PNS shows that power bias subtraction is the most important aspect of PNS-Gabor features to provide an improvement over Mel-Gabor features. The result indicates that Gabor processing compensates the limitation of PNCC for channels with frequency-shift characteristic. Overall, PNS-Gabor features decrease the word error rate by 32% relative to MFCC and 13% relative to PNCC in Aurora2. For noisy WSJ, they decrease the word error rate by 30.9% relative to MFCC and 24.7% relative to PNCC.
Starting Page	7063
Ending Page	7067
File Size	1316831
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6639032
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mel frequency cepstral coefficient Gabor filters Filter banks Noise Speech recognition Noise measurement Robustness large vocabulary speech recognizion spectro-temporal features robust speech recognition
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Robust speech recognition using spectral subtraction and temporal structure normalization

Localized spectro-temporal features for noise-robust speech recognition

Auditory Features Revisited for Robust Speech Recognition

Cepstral noise subtraction for robust automatic speech recognition

Speech recognition features: Comparison studies on robustness against environmental distortions

Easy does it: Robust spectro-temporal many-stream ASR without fine tuning streams

Modulation spectrum power-law expansion for robust speech recognition

Hierarchical spectro-temporal features for robust speech recognition

Auditory features based on Gammatone filters for robust speech recognition

Spectro-temporal features for noise-robust speech recognition using power-law nonlinearity and power-bias subtraction

Similar Documents

Robust speech recognition using spectral subtraction and temporal structure normalization

Localized spectro-temporal features for noise-robust speech recognition

Auditory Features Revisited for Robust Speech Recognition

Cepstral noise subtraction for robust automatic speech recognition

Speech recognition features: Comparison studies on robustness against environmental distortions

Easy does it: Robust spectro-temporal many-stream ASR without fine tuning streams

Modulation spectrum power-law expansion for robust speech recognition

Hierarchical spectro-temporal features for robust speech recognition

Auditory features based on Gammatone filters for robust speech recognition

Spectro-temporal features for noise-robust speech recognition using power-law nonlinearity and power-bias subtraction