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Use of Novel Feature Extraction Technique With Subspace Classifiers for Speech Recognition

Overview Speech recognition is one of the fast moving research areas in pervasive services requiring human interaction. Like any type of pattern recognition system, selection of the feature extraction method and the classifier play a crucial role for speech recognition in terms of accuracy and speed. In this paper, an efficient wavelet based feature extraction method for speech data is presented. The feature vectors are then fed into three widely used linear subspace classifiers for recognition analysis. These classifiers are Class Featuring Information Compression (CLAFIC), Multiple Similarity Method (MSM) and Common Vector Approach (CVA). TI-DIGIT database is used to evaluate the performance of speaker independent isolated word recognition system designed.

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Publisher
Anadolu University
File Format
PDF
Date Published
Dec 13, 2008
Format
White Papers
Topics
Voice Recognition, Data Acquisition - ETL

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