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Conference papers

Frequency and Wavelet Filtering for Robust Speech Recognition

Murat Deviren 1 Khalid Daoudi 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Mel-frequency cepstral coefficients (MFCC) are the most widely used features in current speech recognition systems. However, they have a poor physical interpretation and they do not lie in the frequency domain. Frequency filtering (FF) is a technique that has been recently developed to design frequency-localized speech features that perform similar to MFCC in terms of recognition performances. Motivated by our desire to build time-frequency speech models, we wanted to use the FF technique as front-end. However, when evaluating FF on the Aurora-3 database we found some discrepancies in the highly mismatch case. This led us to put FF in another perspective: the wavelet transform. By doing so, we were able to explain the discrepancies and to achieve significant improvements in recognition in the highly mismatch case.
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Submitted on : Tuesday, September 26, 2006 - 9:40:57 AM
Last modification on : Friday, February 26, 2021 - 3:28:06 PM


  • HAL Id : inria-00099753, version 1



Murat Deviren, Khalid Daoudi. Frequency and Wavelet Filtering for Robust Speech Recognition. Artificial Neural Networks and Neural Information Processing - Joint International Conference ICANN/ICONIP2003, 2003, Istanbul, Turquie, pp.452-462. ⟨inria-00099753⟩



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