NDLI: HMM-Based Reconstruction of Unreliable Spectrographic Data for Noise Robust Speech Recognition

Content Provider	IEEE Xplore Digital Library
Author	Borgström, B.J. Alwan, A.
Copyright Year	2006
Abstract	This paper presents a framework for efficient HMM-based estimation of unreliable spectrographic speech data. It discusses the role of hidden Markov models (HMMs) during minimum mean-square error (MMSE) spectral reconstruction. We develop novel HMM-based reconstruction algorithms which exploit intra-channel (across-time) correlation and/or inter-channel (across-frequency) correlation. For the sake of computational efficiency, this paper utilizes approximations to HMM-based decoding methods by developing models constructed from lower resolution quantizers. State configurations for lower resolution models are obtained through a tree-structured mapping of quantizer centroids, and model parameters are adapted accordingly. HMM downsampling avoids expensive retraining of models, and eliminates unnecessary memory requirements. Explicit general formulae are presented for the adaptation of steady-state and transitional statistics. Adaptation of observation statistics are derived from stochastic models of noise spectral magnitude estimation accuracies. The proposed estimation methods are applied in combination with oracle masks, which provide an upper performance bound, as well as masks derived from speech presence probability, which represent a more realistic scenario. Both methods are shown to boost noise robust recognition accuracies significantly relative to the Mel-frequency cepstral coefficient (MFCC) baseline system. Furthermore, HMM downsampling greatly reduces the complexity of the HMM-based reconstruction method while negligibly affecting results.
Sponsorship	IEEE Signal Processing Society
Starting Page	1612
Ending Page	1623
Page Count	12
File Size	664270
File Format	PDF
ISSN	15587916
Volume Number	18
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Speech recognition Hidden Markov models Reconstruction algorithms Statistics Computational efficiency Decoding Steady-state Stochastic resonance Speech coding spectral reconstruction Hidden Markov models (HMMs) mask estimation missing features (MFs) noise robust speech recognition
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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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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