5th International Conference on Spoken Language Processing
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).
Bibliographic reference. Ganapathiraju, Aravind / Hamaker, Jonathan / Picone, Joseph (1998): "Support vector machines for speech recognition", In ICSLP-1998, paper 0410.