NDLI: Matched-condition robust Dynamic Noise Adaptation

Content Provider	IEEE Xplore Digital Library
Author	Rennie, S.J. Dognin, P.L. Fousek, P.
Copyright Year	2011
Description	Author affiliation: IBM T.J. Watson Research Center, Yorktown Heights, N.Y., U.S.A. (Rennie, S.J.; Dognin, P.L.; Fousek, P.)
Abstract	In this paper we describe how the model-based noise robustness algorithm for previously unseen noise conditions, Dynamic Noise Adaptation (DNA), can be made robust to matched data, without the need to do any system re-training. The approach is to do online model selection and averaging between two DNA models of noise: one that is tracking the evolving state of the background noise, and one clamped to the null mis-match hypothesis. The approach, which we call DNA with (matched) condition detection (DNA-CD), improves the performance of a commerical-grade speech recognizer that utilizes feature-space Maximum Mutual Information (fMMI), boosted MMI (bMMI), and feature-space Maximum Likelihood Linear Regression (fMLLR) compensation by 15% relative at signal-to-noise ratios (SNRs) below 10 dB, and over 8% relative overall.
Starting Page	137
Ending Page	140
File Size	182803
Page Count	4
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163919
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	DNA Noise Speech Hidden Markov models Speech recognition Acoustics Adaptation models Algonquin Dynamic Noise Adaptation (DNA) Noise Robustness Model Adaptation fMLLR fMMI Spectral Subtraction
Content Type	Text
Resource Type	Article

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