Signal representation is crucial for designing a speech recognizer. The feature extractor selects the information to be used by the classifier to perform the recognition. In noisy environments, the data vectors representing the speech signal are changed and the recognizer performance is degraded by two main facts: (1) the mismatch between the training and the recognition conditions and (2) the degradation of the signal to be recognized. In such a situation, the representation of the speech signal plays an important role. In this paper, we analyze the importance of the representation for speech recognition in noise. We apply the Discriminative Feature Extraction (DFE) method to optimize the representation. The experiments presented in this work show that the DFE method, which has been successfully applied in clean environments, leads also to improvements of the speech recognizers in noise.
Cite as: Torre, A.d.l., Peinado, A.M., Rubio, A.J., Garcia, P. (1997) Discriminative feature extraction for speech recognition in noise. Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997), 291-294, doi: 10.21437/Eurospeech.1997-100
@inproceedings{torre97_eurospeech,
author={Angel de la Torre and Antonio M. Peinado and Antonio J. Rubio and Pedro Garcia},
title={{Discriminative feature extraction for speech recognition in noise}},
year=1997,
booktitle={Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997)},
pages={291--294},
doi={10.21437/Eurospeech.1997-100}
}