Ihsan  Al-Hassani, Abdlnaser  Assami, Oumayma  Al-Dakkak, 
    A New Robust Resonance Based Wavelet Decomposition Cepstral Features for
Phoneme Recoszgnition,
    Research Journal of Applied Sciences,
    Volume 14,Issue 8,
    2019,
    Pages 250-257,
    ISSN 1815-932x,
    rjasci.2019.250.257,
    (https://makhillpublications.co/view-article.php?doi=rjasci.2019.250.257)
    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.
    Keywords: phoneme classification;wavelet packet decomposition;speech features;ASR;Q-factor;TIMIT