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Conference papers

Statistical Adaptation of Acoustic Models to Noise Conditions for Robust Speech Recognition

Angel de la Torre 1 Dominique Fohr 1 Jean-Paul Haton 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Noise degrades the performance of Automatic Speech Recognition (ASR) systems working in real condition. The mismatch between the training and recognition conditions is considered the main factor involved in this degradation, and most methods for robust ASR are focussed on its minimization. In this work, we compare robust methods for ASR based on (a) the compensation of the noise effects and (b) the adaptation of the acoustic models to noise con-ditions. We propose a method for the adaptation of the acoustic models to the noise conditions based on a statistical formulation. In this method, each Gaussian is adapted to the noisy environment according to the estimated noise conditions. Recognition experiments have been carried out using speech acquired in real car environments. The results show the statistical formulation for adaptation provides an accurate method for robust ASR.
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Submitted on : Tuesday, September 26, 2006 - 2:52:13 PM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM


  • HAL Id : inria-00100832, version 1



Angel de la Torre, Dominique Fohr, Jean-Paul Haton. Statistical Adaptation of Acoustic Models to Noise Conditions for Robust Speech Recognition. International Conference on Spoken Language Processing - ICSLP 2002, 2002, Denver, USA, pp.1437-1440. ⟨inria-00100832⟩



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