Visually-derived Wiener filters for speech enhancement

Almajai, Ibrahim, Milner, Ben P., Darch, Jonathan and Vaseghi, Saeed V. (2007) Visually-derived Wiener filters for speech enhancement. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007-04-15 - 2007-04-20.

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Abstract

This work begins by examining the correlation between audio and visual speech features and reveals higher correlation to exist within individual phoneme sounds rather than globally across all speech. Utilising this correlation, a visually-derived Wiener filter is proposed in which clean power spectrum estimates are obtained from visual speech features. Two methods of extracting clean power spectrum estimates are made; first from a global estimate using a single Gaussian mixture model (GMM), and second from phoneme-specific estimates using a hidden Markov model (HMM)-GMM structure. Measurement of estimation accuracy reveals that the phoneme-specific (HMM-GMM) system leads to lower estimation errors than the global (GMM) system. Finally, the effectiveness of visually-derived Wiener filtering is examined

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Vishal Gautam
Date Deposited:	04 Apr 2011 12:09
Last Modified:	10 Dec 2024 01:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22606
DOI:	10.1109/ICASSP.2007.366980

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