Normalized Wavelet Hybrid Feature for Consonant Classification in Noisy Environments

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)


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 noisy 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.


Speech Signal Wavelet Transform Recognition Accuracy Automatic Speech Recognition Noisy Environment 


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© Springer India 2013

Authors and Affiliations

  1. 1.Department of Information TechnologyKannur UniversityKannurIndia

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