Accent classification using support vector machine and hidden Markov model

Authors:
Hong Tang;Ali A. Ghorbani
Affiliations:
Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada;Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada
Venue:
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Year:
2003

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Abstract

Accent classification technologies directly influence the performance of speechrecogn ition. Currently, two models are used for accent detection namely: Hidden Markov Model (HMM) and Artificial Neural Networks (ANN). However, both models have some drawbacks of their own. In this paper, we use Support Vector Machine (SVM) to detect different speakers' accents. To examine the performance of SVM, Hidden Markov Model is used to classify the same problem set. Simulation results show that SVM can effectively classify different accents. Its performance is found to be very similar to that of HMM.