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Predicting Head Pose in Dyadic Conversation

  • David Greenwood
  • Stephen Laycock
  • Iain Matthews
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10498)

Abstract

Natural movement plays a significant role in realistic speech animation. Numerous studies have demonstrated the contribution visual cues make to the degree we, as human observers, find an animation acceptable. Rigid head motion is one visual mode that universally co-occurs with speech, and so it is a reasonable strategy to seek features from the speech mode to predict the head pose. Several previous authors have shown that prediction is possible, but experiments are typically confined to rigidly produced dialogue.

Expressive, emotive and prosodic speech exhibit motion patterns that are far more difficult to predict with considerable variation in expected head pose. People involved in dyadic conversation adapt speech and head motion in response to the others’ speech and head motion. Using Deep Bi-Directional Long Short Term Memory (BLSTM) neural networks, we demonstrate that it is possible to predict not just the head motion of the speaker, but also the head motion of the listener from the speech signal.

Keywords

Speech animation Head motion synthesis Visual prosody Dyadic conversation Generative models BLSTM CVAE 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of East AngliaNorwichUK

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