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.
Ihsan Al-Hassani, Oumayma Al-Dakkak and Abdlnaser Assami, 2019. A New Robust Resonance Based Wavelet Decomposition Cepstral Features for Phoneme Recoszgnition. Research Journal of Applied Sciences, 14: 250-257.