Combined Feature Extraction Techniques and Naive Bayes Classifier for Speech Recognition
Sonia Sunny, David Peter S, Poulose Jacob K · 2013
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.