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Related Experiment Video

Updated: Jul 7, 2026

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

An HMM-based speech-to-video synthesizer.

J J Williams1, A K Katsaggelos

  • 1Dept. of Electr. and Comput. Eng., Northwestern Univ., Evanston, IL, USA.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
Summary

This study introduces a novel correlation hidden Markov model (HMM) for synthesizing visual speech from audio. This approach improves speechreading accuracy and reduces training data requirements.

Area of Science:

  • Speech processing
  • Computer vision
  • Multimedia communication

Background:

  • Broadband communication systems enable multimedia telephony, integrating visual information with audio.
  • Synthesizing visual articulatory movements from acoustic speech signals is crucial for enhancing speechreading capabilities.
  • Existing narrowband systems for speech can be leveraged for visual speech synthesis.

Purpose of the Study:

  • To develop a hidden Markov model (HMM)-based visual speech synthesizer for generating articulatory movements from acoustic speech signals.
  • To introduce a novel correlation HMM that integrates independently trained acoustic and visual HMMs for speech-to-visual synthesis.
  • To enhance flexibility in model topology selection and reduce training data needs compared to earlier integration methods.

Main Methods:

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Published on: February 9, 2011

  • Decomposition of the modeling task into key stages for HMM application.
  • Judicious determination of observation vector components for each stage.
  • Development and application of a novel correlation HMM for integrating acoustic and visual HMMs.

Main Results:

  • Objective experiments showed a 37.4% reduction in time alignment errors compared to conventional temporal scaling.
  • Subjective evaluations indicated an increase in speech understanding using the proposed model.
  • The correlation HMM allows for greater flexibility in acoustic and visual HMM topologies.

Conclusions:

  • The proposed correlation HMM effectively synthesizes visual speech from acoustic signals.
  • This method offers improved accuracy and efficiency in visual speech synthesis.
  • The approach has the potential to significantly enhance multimedia communication systems.