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Details of Grant 

EPSRC Reference: GR/T04823/01
Title: Audio-Visual Speech Recognition in the Presence of Non-Stationary Noise
Principal Investigator: Barker, Professor J
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: University of Sheffield
Scheme: First Grant Scheme Pre-FEC
Starts: 28 February 2005 Ends: 27 May 2007 Value (£): 116,853
EPSRC Research Topic Classifications:
Human Communication in ICT Image & Vision Computing
Vision & Senses - ICT appl.
EPSRC Industrial Sector Classifications:
Creative Industries
Related Grants:
Panel History:  
Summary on Grant Application Form
This proposal concerns the development of novel techniques for exploiting visual speech information (e.g. lip and face movements) in the design of automatic speech recognition systems. The approaches explored are motivated by the desire for reliable speech recognition in the presence of highly non-stationary noise sources (e.g. other speakers). The basis of the project will be a new approach to robust automatic speech recognition developed by the proposer that operates by 'piecing together' fragments of speech that can be observed in low noise regions of a time-frequency representation. The proposal extends this approach into the audio visual domain. The audio-visual system will exploit the correlation that exists between audio and visual aspects of speech to resolve ambiguities in the acoustic fragment labelling that occur in the present system when attempting to recognise speech in the presence of noises with speech-like characteristics (e.g. simultaneous speakers). Extending the current system from audio-only to audiovisual is nontrivial; questions about how best to integrate the audio and visual data streams, and how best to exploit the visual information have to be carefully addressed. The research will necessitate integrating several areas of expertise including computational auditory modelling, audio-visual speech perception, and robust automatic speech recognition.
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Organisation Website: http://www.shef.ac.uk