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α-Jacobian environmental adaptation

Identifieur interne : 000364 ( PascalFrancis/Curation ); précédent : 000363; suivant : 000365

α-Jacobian environmental adaptation

Auteurs : Christophe Cerisara [France] ; Luca Rigazio [États-Unis] ; Jean-Claude Junqua [États-Unis]

Source :

RBID : Pascal:04-0274368

Descripteurs français

English descriptors

Abstract

The robustness of automatic speech recognition systems to noise is still a problem, especially for small footprint systems. This paper addresses the problem of noise robustness using model compensation methods. Such algorithms are already available, but their complexity is usually high. An often-referenced method for achieving noise robustness is parallel model combination (PMC). Several algorithms have been proposed to develop more computationally efficient methods than PMC. For example, Jacobian adaptation approximates PMC with a linear transformation function in the cepstral domain. However, the Jacobian approximation is valid only for test environments that are close to the training conditions whereas, in real test conditions, the mismatch between the test and training environments is usually large. In this paper, we propose two methods, respectively called static and dynamic α-Jacobian adaptation (or α-JAC), to compute new linear approximations of PMC for realistic test environments. We further extend both algorithms to compensate for additive and convolutional noise and we derive the corresponding non-linear algorithm that is approximated. All these algorithms are experimentally compared in important mismatch conditions. As compared to Jacobian adaptation, improvements are observed with both static and dynamic α-Jacobian adaptation.
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A08 01  1  ENG  @1 α-Jacobian environmental adaptation
A09 01  1  ENG  @1 Adaptation methods for speech recognition
A11 01  1    @1 CERISARA (Christophe)
A11 02  1    @1 RIGAZIO (Luca)
A11 03  1    @1 JUNQUA (Jean-Claude)
A12 01  1    @1 JUNQUA (J. C.) @9 ed.
A12 02  1    @1 WELLEKENS (C. J.) @9 ed.
A14 01      @1 LORIA, UMR 7503 Campus Scientifique, BP 239 @2 54506 Vandoeuvre-les-Nancy @3 FRA @Z 1 aut.
A14 02      @1 Panasonic Speech Technology Laboratory, 3888 State Street, Suite 202 @2 Santa-Barbara, CA 93105 @3 USA @Z 2 aut. @Z 3 aut.
A15 01      @1 Panasonic Speech Technology Laboratory of Panasonic Tech. Company, Div. of Matsushita Electric Corp. of America, Suite 202, 3888 State Street @2 Santa Barbara, CA 93105 @3 USA @Z 1 aut.
A15 02      @1 Institut Eurécom, Multimedia Communications Dpt, 2229, Route de Cretes - B.P. 193 @2 06904 Sophia Antipolis @3 FRA @Z 2 aut.
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C01 01    ENG  @0 The robustness of automatic speech recognition systems to noise is still a problem, especially for small footprint systems. This paper addresses the problem of noise robustness using model compensation methods. Such algorithms are already available, but their complexity is usually high. An often-referenced method for achieving noise robustness is parallel model combination (PMC). Several algorithms have been proposed to develop more computationally efficient methods than PMC. For example, Jacobian adaptation approximates PMC with a linear transformation function in the cepstral domain. However, the Jacobian approximation is valid only for test environments that are close to the training conditions whereas, in real test conditions, the mismatch between the test and training environments is usually large. In this paper, we propose two methods, respectively called static and dynamic α-Jacobian adaptation (or α-JAC), to compute new linear approximations of PMC for realistic test environments. We further extend both algorithms to compensate for additive and convolutional noise and we derive the corresponding non-linear algorithm that is approximated. All these algorithms are experimentally compared in important mismatch conditions. As compared to Jacobian adaptation, improvements are observed with both static and dynamic α-Jacobian adaptation.
C02 01  X    @0 001D04A05B
C03 01  X  FRE  @0 Reconnaissance parole @5 01
C03 01  X  ENG  @0 Speech recognition @5 01
C03 01  X  SPA  @0 Reconocimiento voz @5 01
C03 02  X  FRE  @0 Reconnaissance automatique @5 02
C03 02  X  ENG  @0 Automatic recognition @5 02
C03 02  X  SPA  @0 Reconocimiento automático @5 02
C03 03  X  FRE  @0 Robustesse @5 03
C03 03  X  ENG  @0 Robustness @5 03
C03 03  X  SPA  @0 Robustez @5 03
C03 04  X  FRE  @0 Méthode Jacobi @5 04
C03 04  X  ENG  @0 Jacobi method @5 04
C03 04  X  SPA  @0 Método Jacobi @5 04
C03 05  X  FRE  @0 Méthode adaptative @5 05
C03 05  X  ENG  @0 Adaptive method @5 05
C03 05  X  SPA  @0 Método adaptativo @5 05
C03 06  X  FRE  @0 Réduction bruit @5 06
C03 06  X  ENG  @0 Noise reduction @5 06
C03 06  X  SPA  @0 Reducción ruido @5 06
C03 07  X  FRE  @0 Bruit additif @5 07
C03 07  X  ENG  @0 Additive noise @5 07
C03 07  X  SPA  @0 Ruido aditivo @5 07
C03 08  X  FRE  @0 Compensation modèle @4 INC @5 72
N21       @1 173
N44 01      @1 PSI
N82       @1 PSI

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Pascal:04-0274368

Le document en format XML

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