NDLI: Acoustic factorisation

Content Provider	IEEE Xplore Digital Library
Author	Gales, M.J.F.
Copyright Year	2001
Description	Author affiliation: Dept. of Eng., Cambridge Univ., UK (Gales, M.J.F.)
Abstract	This paper describes a new technique for training a speech recognition system on inhomogeneous training data. The proposed technique, acoustic factorisation, attempts to model explicitly all the factors that affect the acoustic signal. By explicitly modelling all the factors, the trained model set may be used in a more flexible fashion than in standard adaptive training schemes. Since an individual model is trained for each factor, it is possible to factor-in only those factors that are appropriate to a particular target domain, for example the distribution over all training speakers. The target domain specific factors are simply estimated from limited target specific data, for example the target acoustic environment. The paper describes the theory of this new approach for the transforms for a particular speaker and environment. Initial experiments on a large vocabulary speech recognition task are presented.
Starting Page	77
Ending Page	80
File Size	309329
Page Count	4
File Format	PDF
ISBN	078037343X
DOI	10.1109/ASRU.2001.1034593
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-12-09
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Loudspeakers Speech recognition Maximum likelihood linear regression Acoustic noise Working environment noise Acoustical engineering Data engineering Training data Degradation Acoustic testing
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 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

Improving the Robustness of Persian Large Vocabulary Continuous Speech Recognition System for Real Applications

Adaptive training using structured transforms

Variance compensation within the MLLR framework for robust speech recognition and speaker adaptation

Maximum Likelihood Linear Regression Adaptation for the Polynomial Segment Models

Discriminative acoustic model using eigenspace mapping for rapid speaker adaptation

Iterative unsupervised adaptation using maximum likelihood linear regression

Enrollment in low-resource speech recognition systems

Speaker adaptation using constrained transformation

Tree-structured model selection and simulated-data adaptation for environmental and speaker robust speech recognition

Acoustic factorisation

Similar Documents

Improving the Robustness of Persian Large Vocabulary Continuous Speech Recognition System for Real Applications

Adaptive training using structured transforms

Variance compensation within the MLLR framework for robust speech recognition and speaker adaptation

Maximum Likelihood Linear Regression Adaptation for the Polynomial Segment Models

Discriminative acoustic model using eigenspace mapping for rapid speaker adaptation

Iterative unsupervised adaptation using maximum likelihood linear regression

Enrollment in low-resource speech recognition systems

Speaker adaptation using constrained transformation

Tree-structured model selection and simulated-data adaptation for environmental and speaker robust speech recognition

Acoustic factorisation