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Multiclass support vector machines for articulatory feature classification.
| Content Provider | CiteSeerX |
|---|---|
| Author | Hutchinson, Brian Zhang, Jianna |
| Abstract | This ongoing research project investigates articulatory feature (AF) classification using multiclass support vector machines (SVMs). SVMs are being constructed for each AF in the multi-valued feature set (Table 1), using speech data and annotation from the IFA Dutch “Open-Source ” (van Son et al. 2001) and TIMIT English (Garofolo et al. 1993) 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 (DDAG), and direct methods for multiclass learning. Observing the successful application of SVMs to numerous classification problems (Bennett and Campbell 2000), it is hoped that multiclass SVMs will outperform existing state-of-the-art AF classifiers. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multiclass Support Vector Machine Articulatory Feature Classification Af Classification Performance State-of-the-art Af Classifier Successful Application Multiclass Svms Van Son Speech Data Multi-valued Feature Different Multiclass Generalization Timit English Multiclass Learning Decision Directed Acyclic Graph Numerous Classification Problem Ongoing Research Project Direct Method Articulatory Feature Primary Objective Ifa Dutch Open-source |
| Content Type | Text |
