Framewise phone classification using support vector machines

Jesper Salomon, Simon King, Miles Osborne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We describe the use of Support Vector 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 to ABBOT. We therefore avoid the problems of classifying variable-length vectors. Our frame-level 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.
Original languageEnglish
Title of host publicationICSLP- 2002
Subtitle of host publicationProceedings of the 7th International Conference on Spoken Language Processing
PublisherInternational Speech Communication Association
Number of pages4
ISBN (Print)ISSN: 1990-9772
Publication statusPublished - 2002


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