@article{MAKHILLRJAS201914810191,
    title = {A New Robust Resonance Based Wavelet Decomposition Cepstral Features for
Phoneme Recoszgnition},
    journal = {Research Journal of Applied Sciences},
    volume = {14},
    number = {8},
    pages = {250-257},
    year = {2019},
    issn = {1815-932x},
    doi = {rjasci.2019.250.257},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2019.250.257},
    author = {Ihsan,Abdlnaser and},
    keywords = {phoneme classification,wavelet packet decomposition,speech features,ASR,Q-factor,TIMIT},
    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.}
    }