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HMM-based gain-modeling for enhancement of speech in noise
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2007 (English)In: IEEE transactions on speech and audio processing, ISSN 1063-6676, E-ISSN 1558-2353, Vol. 15, no 3, 882-892 p.Article in journal (Refereed) Published
Abstract [en]

Accurate modeling and estimation of speech and noise gains facilitate good performance of speech. enhancement methods using data-driven prior models. In this paper, we propose a hidden Markov model (HMM)-based speech enhancement method using explicit gain modeling. Through the introduction of stochastic gain variables, energy variation in both speech and noise is explicitly modeled in a unified framework. The speech gain models the energy variations of the speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain helps to improve the tracking of the time-varying energy of nonstationary noise. The expectationmaximization (EM) algorithm is used to perform offline estimation of the time-invariant model parameters. The time-varying model'parameters are estimated online using the recursive EM algorithm. The. proposed gain modeling techniques are applied to a novel Bayesian speech estimator, and the performance of the proposed enhancement method is evaluated through objective and subjective tests. The experimental results confirm the advantage of explicit gain modeling, particularly for nonstationary noise sources.

Place, publisher, year, edition, pages
2007. Vol. 15, no 3, 882-892 p.
Keyword [en]
Gain modeling; Hidden Markov modeling (HMM); Noise suppression; Speech enhancement; Accurate modeling; Bayesian; Data-driven; Energy variations; Expectation-maximization algorithms; Gain modeling; Gain models; Hidden Markov modeling (HMM); Modeling techniques; Noise gains; Noise suppression; Non-stationary noise; Offline; Recursive em; Speech enhancement methods; Subjective tests; Time-invariant models; Time-varying; Time-varying model parameters; Unified frameworks; Polarization; Speech enhancement; Statistical tests; Time varying systems; Hidden Markov models
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-7235DOI: 10.1109/TASL.2006.885256ISI: 000244318600013Scopus ID: 2-s2.0-51449116166OAI: oai:DiVA.org:kth-7235DiVA: diva2:12187
Note
QC 20100825Available from: 2007-05-31 Created: 2007-05-31 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Model Based Speech Enhancement and Coding
Open this publication in new window or tab >>Model Based Speech Enhancement and Coding
2007 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

In mobile speech communication, adverse conditions, such as noisy acoustic environments and unreliable network connections, may severely degrade the intelligibility and natural- ness of the received speech quality, and increase the listening effort. This thesis focuses on countermeasures based on statistical signal processing techniques. The main body of the thesis consists of three research articles, targeting two specific problems: speech enhancement for noise reduction and flexible source coder design for unreliable networks.

Papers A and B consider speech enhancement for noise reduction. New schemes based on an extension to the auto-regressive (AR) hidden Markov model (HMM) for speech and noise are proposed. Stochastic models for speech and noise gains (excitation variance from an AR model) are integrated into the HMM framework in order to improve the modeling of energy variation. The extended model is referred to as a stochastic-gain hidden Markov model (SG-HMM). The speech gain describes the energy variations of the speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain improves the tracking of the time-varying energy of non-stationary noise, e.g., due to movement of the noise source. In Paper A, it is assumed that prior knowledge on the noise environment is available, so that a pre-trained noise model is used. In Paper B, the noise model is adaptive and the model parameters are estimated on-line from the noisy observations using a recursive estimation algorithm. Based on the speech and noise models, a novel Bayesian estimator of the clean speech is developed in Paper A, and an estimator of the noise power spectral density (PSD) in Paper B. It is demonstrated that the proposed schemes achieve more accurate models of speech and noise than traditional techniques, and as part of a speech enhancement system provide improved speech quality, particularly for non-stationary noise sources.

In Paper C, a flexible entropy-constrained vector quantization scheme based on Gaus- sian mixture model (GMM), lattice quantization, and arithmetic coding is proposed. The method allows for changing the average rate in real-time, and facilitates adaptation to the currently available bandwidth of the network. A practical solution to the classical issue of indexing and entropy-coding the quantized code vectors is given. The proposed scheme has a computational complexity that is independent of rate, and quadratic with respect to vector dimension. Hence, the scheme can be applied to the quantization of source vectors in a high dimensional space. The theoretical performance of the scheme is analyzed under a high-rate assumption. It is shown that, at high rate, the scheme approaches the theoretically optimal performance, if the mixture components are located far apart. The practical performance of the scheme is confirmed through simulations on both synthetic and speech-derived source vectors.

Place, publisher, year, edition, pages
Stockholm: KTH, 2007. xii, 49 p.
Series
Trita-EE, ISSN 1653-5146 ; 2007:018
Keyword
statistical model, Gaussian mixture mdel (GMM), hidden Markov model (HMM), moise reduction
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-4412 (URN)978-91-682-7157-4 (ISBN)
Public defence
2007-06-11, Sal FD5, AlbaNova, Roslagstullsbacken 21, Stockholm, 09:15
Opponent
Supervisors
Note
QC 20100825Available from: 2007-05-31 Created: 2007-05-31 Last updated: 2010-08-25Bibliographically approved

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Citation style
  • apa
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Output format
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