International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 4, No. 8, 8 Aug. 2012
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Multi Resolution Analysis for Consonant Classification in Noisy Environments
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Author(s)
T M Thasleema
1,*
N K Narayanan
1
Index Terms
Wavelet Transform, Normalized Wavelet Hybrid Features, Daubechies Wavelet, k – Nearest Neighborhood, Artificial Neural Network
Abstract
This paper investigates on the use of Wavelet Transform (WT) to model and recognize the utterances of Consonant – Vowel (CV) speech units in noisy environments. The peculiarity of the proposed method lies in the fact that using WT, non stationary nature of the speech signal can be accurately considered. A hybrid feature extraction namely Normalized Wavelet Hybrid Feature (NWHF) using the combination of Classical Wavelet Decomposition (CWD) and Wavelet Packet Decomposition (WPD) along with z-score normalization technique are studied here. CV speech unit recognition tasks performed for both noisy and clean speech units using Artificial Neural Network (ANN) and k – Nearest Neighborhood (k – NN) are also presented. The result indicates the robustness of the proposed technique based on WT in additive noisy condition.
Cite This Paper
T M Thasleema, N K Narayanan,"Multi Resolution Analysis for Consonant Classification in Noisy Environments", IJIGSP, vol.4, no.8, pp.15-23, 2012. DOI: 10.5815/ijigsp.2012.08.03
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