NDLI: On separating environmental and speaker adaptation

Content Provider	IEEE Xplore Digital Library
Author	Lima, C. Silva, C. Tavares, A. Oliveira, J.
Copyright Year	2003
Description	Author affiliation: Dept. of Ind. Electron., Univ. of Minho, Portugal (Lima, C.; Silva, C.; Tavares, A.)
Abstract	This paper presents a maximum likelihood (ML) approach, concerned to the background model estimation, in noisy acoustic non-stationary environments. The external noise source is characterised by a time constant convolutional and a time varying additive components. The HMM composition technique, provides a mechanism for integrating parametric models of acoustic background with the signal model, so that noise compensation is tightly coupled with the background model estimation. However, the existing continuous adaptation algorithms usually do not take advantage of this approach, being essentially based on the MLLR algorithm. Consequently, a model for environmental mismatch is not available and, even under constrained conditions a significant number of model parameters have to be updated. From a theoretical point of view only the noise model parameters need to be updated, being the clean speech ones unchanged by the environment. So, it can be advantageous to have a model for environmental mismatch. Additionally separating the additive and convolutional components means a separation between the environmental mismatch and speaker mismatch when the channel does not change for long periods. This approach was followed in the development of the algorithm proposed in this paper. One drawback sometimes attributed to the continuous adaptation approach is that recognition failures originate poor background estimates. This paper also proposes a MAP-like method to deal with this situation.
Starting Page	413
Ending Page	416
File Size	315784
Page Count	4
File Format	PDF
ISBN	0780379462
DOI	10.1109/ISSPA.2003.1224728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-04
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Working environment noise Acoustic noise Maximum likelihood estimation Background noise Additive noise Convolution Loudspeakers Parametric statistics Maximum likelihood linear regression
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	Mr. Siddhartha Mukherjee siddhartha@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	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray 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

Integrated models of signal and background with application to speaker identification in noise

Improved speaker adaptation using speaker dependent feature projections

MAP estimation of online mapping parameters in ensemble speaker and speaking environment modeling

Speaker and gender normalization for continuous-density hidden Markov models

Maximum conditional likelihood linear regression and maximum a posteriori for hidden conditional random fields speaker adaptation

Rapid discriminative acoustic model based on eigenspace mapping for fast speaker adaptation

Speaker adaptation using discriminative linear regression on time-varying mean parameters in trended HMM

Speaker adaptation using Maximum Likelihood General Regression

Rapid speech recognizer adaptation to new speakers

On separating environmental and speaker adaptation

Similar Documents

Integrated models of signal and background with application to speaker identification in noise

Improved speaker adaptation using speaker dependent feature projections

MAP estimation of online mapping parameters in ensemble speaker and speaking environment modeling

Speaker and gender normalization for continuous-density hidden Markov models

Maximum conditional likelihood linear regression and maximum a posteriori for hidden conditional random fields speaker adaptation

Rapid discriminative acoustic model based on eigenspace mapping for fast speaker adaptation

Speaker adaptation using discriminative linear regression on time-varying mean parameters in trended HMM

Speaker adaptation using Maximum Likelihood General Regression

Rapid speech recognizer adaptation to new speakers

On separating environmental and speaker adaptation