The Effect of Real-Time Constraints on Automatic Speech Animation

Websdale, Danny, Taylor, Sarah and Milner, Ben (2018) The Effect of Real-Time Constraints on Automatic Speech Animation. In: Proceedings of Interspeech 2018. UNSPECIFIED, pp. 2479-2483.

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Abstract

Machine learning has previously been applied successfully to speech-driven facial animation. To account for carry-over and anticipatory coarticulation a common approach is to predict the facial pose using a symmetric window of acoustic speech that includes both past and future context. Using future context limits this approach for animating the faces of characters in real-time and networked applications, such as online gaming. An acceptable latency for conversational speech is 200ms and typically network transmission times will consume a significant part of this. Consequently, we consider asymmetric windows by investigating the extent to which decreasing the future context effects the quality of predicted animation using both deep neural networks (DNNs) and bi-directional LSTM recurrent neural networks (BiLSTMs). Specifically we investigate future contexts from 170ms (fully-symmetric) to 0ms (fullyasymmetric …

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 12 Aug 2019 11:30
Last Modified: 10 Jun 2020 23:37
URI: https://ueaeprints.uea.ac.uk/id/eprint/71940
DOI: 10.21437/Interspeech.2018-2066

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