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Adaptive training of hidden Markov models for stylistic walk synthesis

Published: 07 August 2011 Publication History

Abstract

In this extended abstract, we present the use of Hidden Markov Models (HMMs) in order to synthesize walk sequences with a given style using a small amount of training data from the target style. As a first step, a general model of walk is built. Starting from that model, an adaptive training enables to adapt our model to any particular style using only a small amount of training data. This technique, which was originally developed for speaker adaptation in speech synthesis [Zen et al. 2007], enables to reduce the main problem of machine learning techniques which is the large amount of data needed to train each new model, and to adapt models to the exaggerated style variations of our database that were far from an average walk.

Reference

[1]
Zen, H., Nose, T., Yamagishi, J., Sako, S., Black, T. M. A. W., and Tokuda, K. 2007. The HMM-based Speech Synthesis System (HTS) Version 2.0. In Proceedings of the 6th ISCA Workshop on Speech Synthesis, Bonn, Germany, 294--299.

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  • (2013)A New Modular Strategy For Action Sequence Automation Using Neural Networks And Hidden Markov ModelsInternational Journal of System Dynamics Applications10.4018/ijsda.20130701022:3(18-35)Online publication date: Jul-2013

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  1. Adaptive training of hidden Markov models for stylistic walk synthesis

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      cover image ACM Conferences
      SIGGRAPH '11: ACM SIGGRAPH 2011 Posters
      August 2011
      92 pages
      ISBN:9781450309714
      DOI:10.1145/2037715
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      Published: 07 August 2011

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      • Ministry of Region Wallonne under the Numediart research program

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      • (2013)A New Modular Strategy For Action Sequence Automation Using Neural Networks And Hidden Markov ModelsInternational Journal of System Dynamics Applications10.4018/ijsda.20130701022:3(18-35)Online publication date: Jul-2013

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