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标题：Combined Feature Extraction Techniques and Naive Bayes Classifier for Speech Recognition
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作者：Sonia Sunny ; David Peter S ; K. Poulose Jacob 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2013
卷号：3
期号：4
页码：155-163
DOI：10.5121/csit.2013.3416
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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
关键词：Speech Recognition; Soft Thresholding; Discrete Wavelet Transforms; Wavelet Packet ;Decomposition; Naive Bayes Classifier