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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

University of Nebraska - Lincoln

CSE Conference and Workshop Papers

2009

Support vector machine

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Vowel Recognition From Articulatory Position Time-Series Data, Jun Wang, Ashok Samal, Jordan R. Green, Tom D. Carrell Sep 2009

Vowel Recognition From Articulatory Position Time-Series Data, Jun Wang, Ashok Samal, Jordan R. Green, Tom D. Carrell

CSE Conference and Workshop Papers

A new approach of recognizing vowels from articulatory position time-series data was proposed and tested in this paper. This approach directly mapped articulatory position time-series data to vowels without extracting articulatory features such as mouth opening. The input time-series data were time-normalized and sampled to fixed-width vectors of articulatory positions. Three commonly used classifiers, Neural Network, Support Vector Machine and Decision Tree were used and their performances were compared on the vectors. A single speaker dataset of eight major English vowels acquired using Electromagnetic Articulograph (EMA) AG500 was used. Recognition rate using cross validation ranged from 76.07% to 91.32% for …