Cascading appearance-based features for visual voice activity detection

Navarathna, Rajitha, Dean, David B., Lucey, Patrick J., Sridharan, Sridha, & Fookes, Clinton B. (2010) Cascading appearance-based features for visual voice activity detection. In Proceedings of International Conference on Auditory-Visual Speech Processing (AVSP2010), Auditory-visual Speech Processing (AVSP) , The Prince Hakone, Hakone, Kanagawa, pp. 3-7.

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The detection of voice activity is a challenging problem, especially when the level of acoustic noise is high. Most current approaches only utilise the audio signal, making them susceptible to acoustic noise. An obvious approach to overcome this is to use the visual modality. The current state-of-the-art visual feature extraction technique is one that uses a cascade of visual features (i.e. 2D-DCT, feature mean normalisation, interstep LDA). In this paper, we investigate the effectiveness of this technique for the task of visual voice activity detection (VAD), and analyse each stage of the cascade and quantify the relative improvement in performance gained by each successive stage. The experiments were conducted on the CUAVE database and our results highlight that the dynamics of the visual modality can be used to good effect to improve visual voice activity detection performance.

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ID Code: 33214
Item Type: Conference Paper
Refereed: Yes
Keywords: Visual Speech, Voice Activity Detection, CUAVE Database, Static Features, Dynamic Features
ISBN: 9784990547509
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2010 [please consult the authors]
Deposited On: 21 Jul 2010 22:38
Last Modified: 29 Feb 2012 14:31

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