Evolutionary Speech Recognition

Anne Spalanzani 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Automatic speech recognition systems are becoming ever more common and are increasingly deployed in more variable acoustic conditions, by very different speakers. So these systems, generally conceived in a laboratory, must be robust in order to provide optimal performance in real situations. This article explores the possibility of gaining robustness by designing speech recognition systems able to auto-modify in real time, in order to adapt to the changes of acoustic environment. As a starting point, the adaptive capacities of living organisms were considered in relation to their environment. Analogues of these mechanisms were then applied to automatic speech recognition systems. It appeared to be interesting to imagine a system adapting to the changing acoustic conditions in order to remain effective regardless of its conditions of use.
Type de document :
Chapitre d'ouvrage
Grimm and Kroschel. Robust Speech Recognition and Understanding, I-Tech, 2007
Liste complète des métadonnées

https://hal.inria.fr/inria-00178574
Contributeur : Anne Spalanzani <>
Soumis le : jeudi 11 octobre 2007 - 15:42:19
Dernière modification le : vendredi 12 octobre 2018 - 01:18:14

Identifiants

  • HAL Id : inria-00178574, version 1

Collections

Citation

Anne Spalanzani. Evolutionary Speech Recognition. Grimm and Kroschel. Robust Speech Recognition and Understanding, I-Tech, 2007. 〈inria-00178574〉

Partager

Métriques

Consultations de la notice

369