NDLI: Noise aware manifold learning for robust speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Tomar, V.S. Rose, R.C.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada (Tomar, V.S.; Rose, R.C.)
Abstract	This paper considers the application of discriminative manifold learning approaches in feature analysis for automatic speech recognition (ASR). The issue of manifold learning is addressed for feature space dimensionality reduction in domains involving noise corrupted speech. The locality preserving discriminant analysis (LPDA) approach to manifold learning is investigated. This class of techniques exploits the assumption that there is a structural relationship among data vectors which can be maintained by preserving the local relationships among the transformed data vectors. The paper presents a procedure for reducing the impact of varying acoustic conditions on manifold learning. Noise aware manifold learning (NAML) is described as an approach for exploiting estimated background characteristics to define the size of the local neighborhoods used for LPDA feature space transformations. It is shown that NAML significantly reduces the speech recognition WER in a noisy speech recognition task over LPDA, particularly at low signal-to-noise ratios.
Starting Page	7087
Ending Page	7091
File Size	265241
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6639037
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	Manifolds Speech Signal to noise ratio Vectors Kernel Hidden Markov models speech recognition Locality preserving discriminant analysis graph embedding dimensionality reduction
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

Application of a locality preserving discriminant analysis approach to ASR

Efficient manifold learning for speech recognition using locality sensitive hashing

A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition

Orthogonal Discriminant Neighborhood Preserving Embedding for facial expression recognition

Face Recognition Based on Kernel Schur-Orthogonal Neighborhood Preserving Discriminant Embedding

Stochastic features for noise robust speech recognition

Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

A Robust Digit Recognition Model Research with Low Signal Noise Ratio

Discriminant-enhanced neighborhood preserving embedding for dimensionality reduction

Noise aware manifold learning for robust speech recognition

Similar Documents

Application of a locality preserving discriminant analysis approach to ASR

Efficient manifold learning for speech recognition using locality sensitive hashing

A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition

Orthogonal Discriminant Neighborhood Preserving Embedding for facial expression recognition

Face Recognition Based on Kernel Schur-Orthogonal Neighborhood Preserving Discriminant Embedding

Stochastic features for noise robust speech recognition

Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

A Robust Digit Recognition Model Research with Low Signal Noise Ratio

Discriminant-enhanced neighborhood preserving embedding for dimensionality reduction

Noise aware manifold learning for robust speech recognition