Audio-to-Visual Speech Conversion using Deep Neural Networks

Taylor, Sarah, Kato, Akihiro, Milner, Ben and Matthews, Iain (2016) Audio-to-Visual Speech Conversion using Deep Neural Networks. In: Proceedings of the Interspeech Conference 2016. International Speech Communication Association, USA, pp. 1482-1486.

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Abstract

We study the problem of mapping from acoustic to visual speech with the goal of generating accurate, perceptually natural speech animation automatically from an audio speech signal. We present a sliding window deep neural network that learns a mapping from a window of acoustic features to a window of visual features from a large audio-visual speech dataset. Overlapping visual predictions are averaged to generate continuous, smoothly varying speech animation. We outperform a baseline HMM inversion approach in both objective and subjective evaluations and perform a thorough analysis of our results.

Item Type:	Book Section
Faculty \ School:	Faculty of Science Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Visual Computing and Signal Processing Faculty of Science > Research Groups > Smart Emerging Technologies (former - to 2025) Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Cyber Intelligence and Networks
Depositing User:	Pure Connector
Date Deposited:	24 Sep 2016 01:06
Last Modified:	16 Oct 2025 08:33
URI:	https://ueaeprints.uea.ac.uk/id/eprint/60483
DOI:	10.21437/Interspeech.2016-483

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