Binaural sub-band adaptive speech enhancement using artifical neural networks
Speech Communication - Special issue on robust speech recognition
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Subband Approach for Automatic Speaker Recognition: Optimal Division of the Frequency Domain
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Sub-Band Based Recognition of Noisy Speech
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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Speech feature extraction methods are commonly based on time and frequency processing approaches. In this paper, we propose a new framework based on sub-band processing and non-linear prediction. The key idea is to pre-process the speech signal by a filter bank. From the resulting signals, non-linear predictors are computed. The feature extraction method involves the association of different Neural Predictive Coding (NPC) models. We apply this new framework to phoneme classification and experiments carried out with the NTIMIT database show an improvement of the classification rates in comparison with the full-band approach. The new method is also shown to give better performance than the traditional Linear Predictive Coding (LPC), Mel Frequency Cepstral Coding (MFCC) and Perceptual Linear Prediction (PLP) methods.