COMPARISON AND STATISTICAL ANALYSIS OF NAM AND NORMAL SPEECH PROCESSING USING WAVELET TRANSFORM
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 4
Abstract
In this, we present statistical approaches to enhance body-conducted unvoiced speech for silent speech communication using wavelet transform. So far Analysis of NAM speech has been made only using HMM (Hidden Markov Model) and GMM (Gaussian Markov Model). In this paper, study of analyzing normal speech and NAM speech will be made by using Wavelet transform. Because, wavelets have the great advantage of being able to separate the fine details in a signal. It allows complex information to be decomposed into elementary forms at different positions and subsequently reconstruct it with high precision. Upon analyzing the Normal speech, NAM speech will also be analyzed using the same wavelet transform. The accuracy of words can be obtained from two methods that will be compared to determine the efficiency of using Wavelet transform in recognizing NAM speech. Wavelet analysis is capable of revealing aspects of data that other speech signal analysis technique such the extracted features are then passed to a classifier for the recognition of isolated words.
Authors and Affiliations
Janani P, Devi N
Keywords
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