HMM INVERSION WITH FULL AND DIAGONAL COVARIANCE MATRICES FOR AUDIO-TO-VISUAL CONVERSION

Lucas D. Terissi, Juan C. Gómez

Abstract

A speech driven MPEG-4 compliant facial animation system is proposed in this paper. The main feature of the system is the audio-to-visual conversion based on the inversion of an Audio-Visual Hidden Markov Model. The Hidden Markov Model Inversion algorithm is derived for the general case of considering full covariance matrices for the audio-visual observations. A performance comparison with the more common case of considering diagonal covariance matrices is carried out. Experimental results show that the use of full covariance matrices is preferable since it leads to an accurate estimation of the visual parameters, yielding the same performance as in the case of using diagonal covariance matrices, but with a less complex model.

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Paper Citation


in Harvard Style

D. Terissi L. and C. Gómez J. (2008). HMM INVERSION WITH FULL AND DIAGONAL COVARIANCE MATRICES FOR AUDIO-TO-VISUAL CONVERSION . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008) ISBN 978-989-8111-60-9, pages 168-173. DOI: 10.5220/0001941001680173


in Bibtex Style

@conference{sigmap08,
author={Lucas D. Terissi and Juan C. Gómez},
title={HMM INVERSION WITH FULL AND DIAGONAL COVARIANCE MATRICES FOR AUDIO-TO-VISUAL CONVERSION},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)},
year={2008},
pages={168-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001941001680173},
isbn={978-989-8111-60-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)
TI - HMM INVERSION WITH FULL AND DIAGONAL COVARIANCE MATRICES FOR AUDIO-TO-VISUAL CONVERSION
SN - 978-989-8111-60-9
AU - D. Terissi L.
AU - C. Gómez J.
PY - 2008
SP - 168
EP - 173
DO - 10.5220/0001941001680173