Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement

Tran Huy DAT; Kazuya TAKEDA; Fumitada ITAKURA

doi:10.1093/ietisy/e89-d.3.1040

Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement

Tran Huy DAT, Kazuya TAKEDA, Fumitada ITAKURA

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This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.3 pp.1040-1049

Publication Date: 2006/03/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.3.1040

Type of Manuscript: Special Section PAPER (Special Section on Statistical Modeling for Speech Processing)

Category: Speech Enhancement

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Tran Huy DAT, Kazuya TAKEDA, Fumitada ITAKURA, "Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 3, pp. 1040-1049, March 2006, doi: 10.1093/ietisy/e89-d.3.1040.
Abstract: This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.3.1040/_p

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@ARTICLE{e89-d_3_1040,
author={Tran Huy DAT, Kazuya TAKEDA, Fumitada ITAKURA, },
journal={IEICE TRANSACTIONS on Information},
title={Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement},
year={2006},
volume={E89-D},
number={3},
pages={1040-1049},
abstract={This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.},
keywords={},
doi={10.1093/ietisy/e89-d.3.1040},
ISSN={1745-1361},
month={March},}

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TY - JOUR
TI - Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement
T2 - IEICE TRANSACTIONS on Information
SP - 1040
EP - 1049
AU - Tran Huy DAT
AU - Kazuya TAKEDA
AU - Fumitada ITAKURA
PY - 2006
DO - 10.1093/ietisy/e89-d.3.1040
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2006
AB - This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.
ER -