NDLI: Missing-feature approaches in speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Raj, B. Stern, R.M.
Copyright Year	1991
Description	In this article we have reviewed a wide variety of techniques based on the identification of missing spectral features that have proved effective in reducing the error rates of automatic speech recognition systems. These approaches have been conspicuously effective in ameliorating the effects of transient maskers such as impulsive noise or background music. We described two broad classes of missing feature algorithms: feature-vector imputation algorithms (which restore unreliable components of incoming feature vectors) and classifier-modification algorithms (which dynamically reconfigure the classifier itself to cope with the effects of unreliable feature components). We reviewed the mathematics of four major missing feature techniques: the feature-imputation techniques of cluster-based reconstruction and covariance-based reconstruction, and the classifier-modification methods of class-conditional imputation and marginalization. We also discussed the ways in which the common feature extraction procedures of cepstral analysis, temporal-difference features, and mean subtraction can be handled by speech recognition systems that make use of missing feature techniques. We concluded with a discussion of a small number of selected experimental results. These results confirm the effectiveness of all types of missing feature approaches discussed in ameliorating the effects of both stationary and transient noise, as well as the particular effectiveness of both soft masks and fragment decoding.
Starting Page	101
Ending Page	116
Page Count	16
File Size	841351
File Format	PDF
ISSN	10535888
Volume Number	22
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Clustering algorithms Error analysis Automatic speech recognition Background noise Heuristic algorithms Mathematics Feature extraction Cepstral analysis Decoding
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing Electrical and Electronic Engineering

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

Recognition of Reverberant Speech Using Frequency Domain Linear Prediction

Robust speaker recognition: a feature-based approach

Feature Compensation Incorporating Modeling Error Statistics

Channel identification and signal spectrum estimation for robust automatic speech recognition

Phone-dependent channel compensated hidden Markov model for telephone speech recognition

Analysis and comparison of two speech feature extraction/compensation algorithms

Speech-Signal-Based Frequency Warping

Large-margin feature adaptation for automatic speech recognition

Flatten a Curved Space by Kernel [Applications Corner]

Missing-feature approaches in speech recognition

Similar Documents

Recognition of Reverberant Speech Using Frequency Domain Linear Prediction

Robust speaker recognition: a feature-based approach

Feature Compensation Incorporating Modeling Error Statistics

Channel identification and signal spectrum estimation for robust automatic speech recognition

Phone-dependent channel compensated hidden Markov model for telephone speech recognition

Analysis and comparison of two speech feature extraction/compensation algorithms

Speech-Signal-Based Frequency Warping

Large-margin feature adaptation for automatic speech recognition

Flatten a Curved Space by Kernel [Applications Corner]

Missing-feature approaches in speech recognition