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Contour mapping for speaker-independent lip reading system
Author(s): Souheil Fenghour; Daqing Chen; Perry Xiao
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Paper Abstract

In this paper, we demonstrate how an existing deep learning architecture for automatically lip reading individuals can be adapted it so that it can be made speaker independent, and by doing so, improved accuracies can be achieved on a variety of different speakers. The architecture itself is multi-layered consisting of a convolutional neural network, but if we are to apply an initial edge detection-based stage to pre-process the image inputs so that only the contours are required, the architecture can be made to be less speaker favourable. The neural network architecture achieves good accuracy rates when trained and tested on some of the same speakers in the ”overlapped speakers” phase of simulations, where word error rates of just 1.3% and 0.4% are achieved when applied to two individual speakers respectively, as well as character error rates of 0.6% and 0.3%. The ”unseen speakers” phase fails to achieve as good an accuracy, with greater recorded word error rates of 20.6% and 17.0% when tested on the two speakers with character error rates of 11.5% and 8.3%. The variation in size and colour of different people’s lips will result in different outputs at the convolution layer of a convolutional neural network as the output depends on the pixel intensity of the red, green and blue channels of an input image so a convolutional neural network will naturally favour the observations of the individual whom the network was tested on. This paper proposes an initial ”contour mapping stage” which makes all inputs uniform so that the system can be speaker independent.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104114 (15 March 2019); doi: 10.1117/12.2522936
Show Author Affiliations
Souheil Fenghour, London South Bank Univ. (United Kingdom)
Daqing Chen, London South Bank Univ. (United Kingdom)
Perry Xiao, London South Bank Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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