Support vector machines for speech recognition
نویسندگان
چکیده
A Support Vector Machine (SVM) is a promising machine learning technique that has generated a lot of interest in the pattern recognition community in recent years. The greatest asset of an SVM is its ability to construct nonlinear decision regions in a discriminative fashion. This paper describes an application of SVMs to two speech data classification experiments: 11 vowels spoken in isolation and 16 phones extracted from spontaneous telephone speech. The best performance achieved on the spontaneous speech classification task is a 51% error rate using an RBF kernel. This is comparable to frame-level classification achieved by other nonlinear modeling techniques such as artificial neural networks (ANN).
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تاریخ انتشار 1998