Output details
15 - General Engineering
Brunel University London
Analysis and prediction of acoustic speech features from mel-frequency cepstral coefficients in distributed speech recognition architectures
This collaborative work developed methods that enable speech features to be predicted from mel-frequency cepstral coefficient (MFCC) vectors as may be encountered in distributed speech recognition architectures. Experimental results are presented across a range of conditions, such as with speaker-dependent, gender-dependent, and gender-independent constraints, and these show that acoustic speech features can be predicted from MFCC vectors with good accuracy. A comparison is also made against an alternative scheme that substitutes the higher-order MFCCs with acoustic features for transmission.