Abstract
Accent classification technologies directly influence the performance of speech recognition. 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.
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References
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© 2003 Springer-Verlag Berlin Heidelberg
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Tang, H., Ghorbani, A.A. (2003). Accent Classification Using Support Vector Machine and Hidden Markov Model. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_65
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DOI: https://doi.org/10.1007/3-540-44886-1_65
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