Improved speaker independent lip reading using speaker adaptive training and deep neural networks

Almajai, Ibrahim, Cox, Stephen, Harvey, Richard ORCID: https://orcid.org/0000-0001-9925-8316 and Lan, Yuxuan (2016) Improved speaker independent lip reading using speaker adaptive training and deep neural networks. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). The Institute of Electrical and Electronics Engineers (IEEE), pp. 2722-2726.

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Abstract

Recent improvements in tracking and feature extraction mean that speaker-dependent lip-reading of continuous speech using a medium size vocabulary (around 1000 words) is realistic. However, the recognition of previously unseen speakers has been found to be a very challenging task, because of the large variation in lip-shapes across speakers and the lack of large, tracked databases of visual features, which are very expensive to produce. By adapting a technique that is established in speech recognition but has not previously been used in lip-reading, we show that error-rates for speaker-independent lip-reading can be very significantly reduced. Furthermore, we show that error-rates can be even further reduced by the additional use of Deep Neural Networks (DNN). We also find that there is no need to map phonemes to visemes for context-dependent visual speech transcription.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Science
Depositing User: Pure Connector
Date Deposited: 11 May 2017 05:08
Last Modified: 22 Oct 2022 00:01
URI: https://ueaeprints.uea.ac.uk/id/eprint/63479
DOI: 10.1109/ICASSP.2016.7472172

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