Least Squares Support Vector Machine based Phoneme Recognition
نویسندگان
چکیده
Support Vector Machines (SVMs) have become a popular classification tool. Because of their theoretical robustness they offer improvements in pattern classification applications. This paper describes an approach of producing a N-best list of hypotheses for the needs of phoneme recognition, using a Least Squares Support Vector Machine classifier (LS-SVM) and generate the corresponding N-best list of recognized phonemes. The present LS-SVM approach has the potential to improve the accuracy of the existing phoneme recognizers combining with language model and be applied to Language Identification (LID) systems.
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