Noise robust speech parameterization using multiresolution feature extraction.

Noise robust speech parameterization using multiresolution feature extraction.

In this paper, we present a multiresolution-based feature extraction technique for speech recognition in adverse conditions. The proposed front-end algorithm uses mel cepstrum-based feature computation of subbands in order not to spread noise distortions over the entire feature space. Conventional f...

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Detalhes bibliográficos
Publicado no:IEEE Transactions on speech and audio processing 9, 8 (2001).
Autor principal: Hariharan, R.
Formato: Artigo
Idioma:English
Assuntos:
HMM parameters.
PCA transform.
SNR.
Cepstral domain feature vector normalization.
Computational complexity reduction.
Connected digit recognition task.
Feature vector dimension.
Front-end algorithm.
Long-term spectral information.
Mel cepstrum-based feature computation.
Multiresolution-based feature extraction.
Noise conditions.
Noise robustness.
Speech parameterization.
Speech recognition.
Subbands full-band features.
Word recognition accuracy.