NDLI: New approaches for domain transformation and parameter combination for improved accuracy in parallel model combination (PMC) techniques

Content Provider	IEEE Xplore Digital Library
Author	Jeih-weih Hung Jia-lin Shen Lin-shan Lee
Copyright Year	1993
Abstract	Parallel model combination (PMC) techniques have been very successful and popularly used in many applications to improve the performance of speech recognition systems under noisy environments. However, it is believed that some assumptions and approximations made in this approach, primarily in the domain transformation and parameter combination processes, are not necessarily accurate enough in certain practical situations, which may degrade the achievable performance of PMC. In this paper, the possible sources that cause the performance degradation in these processes are carefully analyzed and discussed. Three new approaches, including the truncated Gaussian approach and the split mixture approach for the domain transformation process and the estimated cross-term approach for parameter combination process, are proposed in this paper in order to handle these problems, minimize such degradation, and improve the accuracy of the PMC techniques. These proposed approaches were analyzed and discussed with two recognition tasks, one relatively simple, and the other more complicated and realistic. Both sets of experiments showed that these proposed approaches are able to provide significant improvements over the original PMC method, especially when the SNR condition is worse.
Starting Page	842
Ending Page	855
Page Count	14
File Size	284360
File Format	PDF
ISSN	10636676
Volume Number	9
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Degradation Working environment noise Cepstral analysis Noise robustness Character recognition Maximum likelihood linear regression Performance analysis Additive noise Loudspeakers
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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