Abstract:
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.