Applying noise compensation methods to robustly predict acoustic speech features from MFCC vectors in noise

Milner, Ben P., Darch, Jonathan and Vaseghi, Saeed V. (2008) Applying noise compensation methods to robustly predict acoustic speech features from MFCC vectors in noise. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2008-03-31 - 2008-04-04.

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Abstract

This paper examines the effect of applying noise compensation to improve acoustic speech feature prediction from noise contaminated MFCC vectors, as may be encountered in distributed speech recognition (DSR). A brief review of maximum a posteriori prediction of acoustic speech features (voicing, fundamental and formant frequencies) from MFCC vectors is made. Two noise compensation methods are then applied; spectral subtraction and model adaptation. Spectral subtraction is used to filter noise from the received MFCC vectors, while model adaptation is applied to adapt the joint models of acoustic features and MFCCs to account for noise contamination. Experiments examine acoustic feature prediction accuracy in noise and results show that the two noise compensation methods significantly improve prediction accuracy in noise. The technique of model adaptation was found to be better than spectral subtraction and could restore performance close to that achieved in matched training and testing.

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:	01 Mar 2011 08:40
Last Modified:	10 Dec 2024 01:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22042
DOI:	10.1109/ICASSP.2008.4518517

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