Framewise phone classification using support vector machines
We describe the use of SupportVector Machines for phonetic classification on the TIMIT corpus. Unlike previous work, in which entire phonemes are classified, our system operates in a framewise manner and is intended for use as the front-end of a hybrid system similar toABBOT. We therefore avoid the problems of classifying variable-length vectors. Our framelevel phone classification accuracy on the complete TIMIT test set is competitive with other results from the literature. In addition, we address the serious problem of scaling Support Vector Machines by using the Kernel Fisher Discriminant.