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Cox, Stephen, Harvey, Richard 
ORCID: https://orcid.org/0000-0001-9925-8316, Lan, Yuxuan, Newman, Jacob and Theobald, Barry-John
(2008)
The Challenge of Multispeaker Lip-Reading.
In: International Conference on Auditory-Visual Speech Processing, 2008-09-26 - 2008-09-29.
Abstract
In speech recognition, the problem of speaker variability has been well studied. Common approaches to dealing with it include normalising for a speaker's vocal tract length and learning a linear transform that moves the speaker-independent models closer to to a new speaker. In pure lip-reading (no audio) the problem has been less well studied. Results are often presented that are based on speaker-dependent (single speaker) or multispeaker (speakers in the test-set are also in the training-set) data, situations that are of limited use in real applications. This paper shows the danger of not using different speakers in the trainingand test-sets. Firstly, we present classification results on a new single-word database AVletters 2 which is a high-definition version of the well known AVletters database. By careful choice of features, we show that it is possible for the performance of visual-only lip-reading to be very close to that of audio-only recognition for the single speaker and multi-speaker configurations. However, in the speaker independent configuration, the performance of the visual-only channel degrades dramatically. By applying multidimensional scaling (MDS) to both the audio features and visual features, we demonstrate that lip-reading visual features, when compared with the MFCCs commonly used for audio speech recognition, have inherently small variation within a single speaker across all classes spoken. However, visual features are highly sensitive to the identity of the speaker, whereas audio features are relatively invariant.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| 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 |
| Related URLs: | |
| Depositing User: | Nicola Talbot |
| Date Deposited: | 14 Mar 2011 09:04 |
| Last Modified: | 10 Dec 2024 01:14 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/26021 |
| DOI: |
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