Comparing noise compensation methods for robust prediction of acoustic speech features from MFCC vectors in noise

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Milner, Ben, Darch, Jonathan, Almajai, Ibrahim and Vaseghi, Saeed (2008) Comparing noise compensation methods for robust prediction of acoustic speech features from MFCC vectors in noise. In: 16th European Signal Processing Conference, 2008-08-25 - 2008-08-29.

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Abstract

The aim of this paper is to investigate the effect of applying noise compensation methods to acoustic speech feature prediction from MFCC vectors, as may be required in a distributed speech recognition (DSR) architecture. A brief review is made of maximum a posteriori (MAP) prediction of acoustic features from MFCC vectors using both global and phoneme-specific modeling of speech. The application of spectral subtraction and model adaptation to MAP acoustic feature prediction is then introduced. Experimental results are presented to compare the effect of noise compensation on acoustic feature prediction accuracy using both the global and phoneme-specific systems. Results across a range of signal-to-noise ratios show model adaptation to be better than spectral subtraction and able to 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 > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.eurasip.org/Proceedings/Eusip...
Depositing User:	Vishal Gautam
Date Deposited:	11 Mar 2011 16:25
Last Modified:	10 Dec 2024 01:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/23774
DOI:

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