Multiclass Support Vector Machines for Articulatory Feature Classification

This ongoing research project investigates articulatory feature (AF) classification using multiclass support vector machines (SVMs). SVMs are being constructed for each AF in a multi-valued feature set, using speech data and annotation from the IFA Dutch "Open-Source" and TIMIT English corpora. The primary objective of this research is to assess the AF classification performance of different multiclass generalizations of the SVM, including one-versus-rest, one-versus-one, Decision Directed Acyclic Graph, and direct methods for multiclass learning. Observing the successful application of SVMs to numerous classification problems, it is hoped that multiclass SVMs will outperform existing state-of-the-art AF classifiers.

Authors

• Brian Hutchinson

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