Alpha-Jacobian environmental adaptation - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Speech Communication Année : 2004

Alpha-Jacobian environmental adaptation

Résumé

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 alpha-Jacobian adaptation (or alpha-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 alpha-Jacobian adaptation.
Fichier non déposé

Dates et versions

inria-00100034 , version 1 (26-09-2006)

Identifiants

  • HAL Id : inria-00100034 , version 1

Citer

Christophe Cerisara, Luca Rigazio, Jean-Claude Junqua. Alpha-Jacobian environmental adaptation. Speech Communication, 2004, 42 (1), pp.25-41. ⟨inria-00100034⟩
113 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More