Analysing the importance of different visual feature coefficients

Milner, Ben and Websdale, Danny (2015) Analysing the importance of different visual feature coefficients. In: FAAVSP - The 1st Joint Conference on Facial Analysis, Animation and Auditory-Visual Speech Processing, 2015-09-11 - 2015-09-13, Austria.

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Abstract

A study is presented to determine the relative importance of different visual features for speech recognition which includes pixel-based, model-based, contour-based and physical features. Analysis to determine the discriminability of features is per- formed through F-ratio and J-measures for both static and tem- poral derivatives, the results of which were found to correlate highly with speech recognition accuracy (r = 0.97). Princi- pal component analysis is then used to combine all visual fea- tures into a single feature vector, of which further analysis is performed on the resulting basis functions. An optimal feature vector is obtained which outperforms the best individual feature (AAM) with 93.5 % word accuracy.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Pure Connector
Date Deposited:	22 Dec 2015 17:05
Last Modified:	10 Dec 2024 01:15
URI:	https://ueaeprints.uea.ac.uk/id/eprint/55875
DOI:

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