Performance Analysis of Different Wavelet Families in Recognizing Speech
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2013 by IJETT Journal|
|Year of Publication : 2013|
|Authors : Sonia Sunny , David Peter S , K Poulose Jacob|
Sonia Sunny , David Peter S , K Poulose Jacob. "Performance Analysis o f Different Wavelet Families i n Recognizing Speech". International Journal of Engineering Trends and Technology (IJETT). V4(4):512-517 Apr 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Automatic Speech Recognition (ASR) is one of the challenging areas of research in digital signal processing and engineering due to its wide range of applications. In this paper, a speech recognition system is developed for recognizing speaker independent spoken isolated words in Malayalam. Voice signals are sampled directly from the microphone and the features are extracted using Discrete Wavelet Transforms (DWT). Different types of wavelet families are available for speech processing and mathematical analysis. Since DWT uses wavelets, the main issue here is to find out the optimal wavelets for speech recognition. This paper investigates the performance of different wavelet families like Haar, Daubechies, Symlets, Coiflets etc. A multi - layer neural network trained with back propagation algorithm is used for classification. The proposed method is implemented for 1000 speakers uttering 10 isolated words each. The experimental results show different recognition accuracies for different wavelet families and the best result of 90.2% is obtained using Daubechies wavelet families with order 4.
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Speech Recognition , Feature Extraction , Wavelet F amilies, Discrete Wavelet Transforms, Classification, Artificial Neural Networks .