NDLI: Robust Speech Recognition From Noise-Type Based Feature Compensation and Model Interpolation in a Multiple Model Framework

Content Provider	IEEE Xplore Digital Library
Author	Haitian Xu Zheng-Hua Tan Dalsgaard, P. Lindberg, B.
Copyright Year	2006
Description	Author affiliation: Center for TeleInFrastructure, Aalborg Univ. (Haitian Xu; Zheng-Hua Tan; Dalsgaard, P.; Lindberg, B.)
Abstract	Compared to multi-condition training (MTR), condition-dependent training generates multiple acoustic hidden Markov model sets each identified by a noisy environment and is known to perform substantially better for known noise types (included in training) while worse for unknown (untrained) noise types. This paper attempts to bridge the performance gap between known and unknown noise types by introducing a minimum mean-square error (MMSE) noise-type based compensation algorithm. On the basis of a modified vector Taylor series and the measurement of feature reliability as well as noise similarity, the MMSE estimation adapts the test features corrupted by the unknown noise type to the corresponding features corrupted by the known noise type. This method significantly improves the recognition performance for unknown noise types while maintaining the good performance for known noise types. Furthermore, in order to benefit directly from MTR, a model interpolation strategy is investigated which combines the MTR and the condition-dependent model sets. Both good performance and low computational cost are achieved by only interpolating the mixtures of each condition-dependent model state with the least weighted mixture in the corresponding MTR model state. The overall system gives promising results
Sponsorship	The Inst. of Electr. and Electron. Eng., Inc. Circuits and Syst. Soc. (IEEE CASS)
File Size	152885
File Format	PDF
ISBN	142440469X
ISSN	15206149
DOI	10.1109/ICASSP.2006.1660227
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-05-14
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Speech recognition Interpolation Working environment noise Acoustic noise Hidden Markov models Noise generators Bridges Taylor series Noise measurement
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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
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