Poulose Jacob,K; Sonia, Sunny; David, Peter S(Computer Science, 2013)
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Abstract:
Speech processing and consequent recognition are important areas of Digital Signal Processing
since speech allows people to communicate more natu-rally and efficiently. In this work, a
speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing
speech, features are to be ex-tracted from speech and hence feature extraction method plays an
important role in speech recognition. Here, front end processing for extracting the features is
per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and
Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose.
After classification using Naive Bayes classifier, DWT produced a recognition accuracy of
83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new
feature extraction method which produces improvements in the recognition accuracy. So, a new
method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes
the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated
and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.
Description:
Computer Science & Information Technology (CS & IT)