Finding phonemes: improving machine lip-reading

Helen L. Bear, Richard Harvey, Yuxuan Lan

Research output: Contribution to conferencePaper

Abstract

In machine lip-reading there is continued debate and research around the correct classes to be used for recognition.

In this paper we use a structured approach for devising speaker-dependent viseme classes, which enables the creation of a set of phoneme-to-viseme maps where each has a different quantity of visemes ranging from two to 45. Viseme classes are based upon the mapping of articulated phonemes, which have been confused during phoneme recognition, into viseme groups.

Using these maps, with the LiLIR dataset, we show the effect of changing the viseme map size in speaker-dependent machine lip-reading, measured by word recognition correctness and so demonstrate that word recognition with phoneme classifiers is not just possible, but often better than word recognition with viseme classifiers. Furthermore, there are intermediate units between visemes and phonemes which are better still.
Original languageEnglish
Publication statusPublished - Sep 2015
EventFAAVSP - The 1st Joint Conference on Facial Analysis, Animation and Auditory-Visual Speech Processing - Austria, Vienna, Austria
Duration: 11 Sep 201513 Sep 2015

Conference

ConferenceFAAVSP - The 1st Joint Conference on Facial Analysis, Animation and Auditory-Visual Speech Processing
Abbreviated titleFAAVSP 2015
CountryAustria
CityVienna
Period11/09/1513/09/15

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