@inproceedings{d2f6db3525684796bec4545ac67eb345,
title = "Vowel recognition from continuous articulatory movements for speaker-dependent applications",
abstract = "A novel approach was developed to recognize vowels from continuous tongue and lip movements. Vowels were classified based on movement patterns (rather than on derived articulatory features, e.g., lip opening) using a machine learning approach. Recognition accuracy on a single-speaker dataset was 94.02% with a very short latency. Recognition accuracy was better for high vowels than for low vowels. This finding parallels previous empirical findings on tongue movements during vowels. The recognition algorithm was then used to drive an articulation-to-acoustics synthesizer. The synthesizer recognizes vowels from continuous input stream of tongue and lip movements and plays the corresponding sound samples in near real-time.",
keywords = "Articulation, Machine learning, Recognition, Support vector machine",
author = "Jun Wang and Green, {Jordan R.} and Ashok Samal and Carrell, {Tom D.}",
year = "2010",
doi = "10.1109/ICSPCS.2010.5709716",
language = "English (US)",
isbn = "9781424479078",
series = "4th International Conference on Signal Processing and Communication Systems, ICSPCS'2010 - Proceedings",
booktitle = "4th International Conference on Signal Processing and Communication Systems, ICSPCS'2010 - Proceedings",
note = "4th International Conference on Signal Processing and Communication Systems, ICSPCS'2010 ; Conference date: 13-12-2010 Through 15-12-2010",
}