Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
Abstract
Robust Automatic Speech Recognition (ASR) is a challenging task that has been an active research subject for the last 20 years. And still results are very modest in the highly noisy environments. In this study, we propose a new speech parameterization method based on concatenating two wavelet packet decompositions, one decomposition using low Q-factor wavelet and another with high Q-factor wavelet, to extract speech features suitable for ASR task in noisy conditions. Experiments on TIMIT dataset for phonemes recognition show that the proposed wavelet-based features outperform MFCC in all noisy conditions.
How to cite this article:
Abdlnaser Assami, Oumayma Al-Dakkak and Ihsan Al-Hassani. A New Robust Resonance Based Wavelet Decomposition Cepstral Features for
Phoneme Recoszgnition.
DOI: https://doi.org/10.36478/rjasci.2019.250.257
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2019.250.257