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.
|Number of pages||4|
|Publication status||Published - 2008|
|Event||IEEE International Conference on Acoustics, Speech and Signal Processing - Las Vegas, United States|
Duration: 31 Mar 2008 → 4 Apr 2008
|Conference||IEEE International Conference on Acoustics, Speech and Signal Processing|
|Period||31/03/08 → 4/04/08|