Research Article | OPEN ACCESS
Noise Robust Speech Parameterization using Relative Spectra and Auditory Filterbank
Youssef Zouhir and Kais Ouni
Signals and Mechatronic Systems, SMS, UR13ES49, National Engineering School of Carthage, ENICarthage, University of Carthage, Tunisia
Research Journal of Applied Sciences, Engineering and Technology 2015 9:755-759
Received: 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).
Keywords:
Auditory filterbank , hidden Markov models , noisy speech parameterization,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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