Vowel recognition from articulatory position time-series data

Jun Wang, Ashok Samal, Jordan R. Green, Tom D. Carrell

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

5 Scopus citations

Abstract

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 the three classifiers. In addition, the trained decision trees were consistent with articulatory features commonly used to descriptively distinguish vowels in classical phonetics. The findings are intended to improve the accuracy and response time of a real-time articulatory-to-acoustics synthesizer.

Original languageEnglish (US)
Title of host publication3rd International Conference on Signal Processing and Communication Systems, ICSPCS'2009 - Proceedings
DOIs
StatePublished - 2009
Event3rd International Conference on Signal Processing and Communication Systems, ICSPCS'2009 - Omaha, NE, United States
Duration: Sep 28 2009Sep 30 2009

Publication series

Name3rd International Conference on Signal Processing and Communication Systems, ICSPCS'2009 - Proceedings

Conference

Conference3rd International Conference on Signal Processing and Communication Systems, ICSPCS'2009
CountryUnited States
CityOmaha, NE
Period9/28/099/30/09

Keywords

  • Articulatory speech recognition
  • Decision tree
  • Neural network
  • Support vector machine
  • Time-series

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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