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     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
2015(Vol.9, Issue:9)
Article Information:

Noise Robust Speech Parameterization using Relative Spectra and Auditory Filterbank

Youssef Zouhir and Kaïs Ouni
Corresponding Author:  Youssef Zouhir 
Submitted: ‎October 05, ‎2014
Accepted: November ‎3, ‎2014
Published: March 25, 2015
Abstract:
In the present study, a new feature extraction method based on relative spectra and gammachirp auditory filterbank is proposed for robust noisy speech recognition. The relative spectra filtering are applied to the log of the output of the gammachirp filterbank which incorporates the properties of the cochlear filter in order to remove uncorrelated additive noise components. The performances of this method have been evaluated on the isolated speech word corrupted by real-world noisy environments using the continuous Gausian-Mixture density Hidden Markov Model. The evaluation of the experimental results shows that the proposed method achieves best recognition rates compared to the conventional techniques like Perceptual Linear Prediction (PLP), Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC).

Key words:  Auditory filterbank, hidden Markov models, noisy speech parameterization, , , ,
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Cite this Reference:
Youssef Zouhir and Kaïs Ouni, . Noise Robust Speech Parameterization using Relative Spectra and Auditory Filterbank . Research Journal of Applied Sciences, Engineering and Technology, (9): 755-759.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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