Multiple application of the MLLT based on clustering supported by phonetic knowledge

This paper intends to show the effect of multiple use of a Maximum Likelihood Linear Transform (MLLT) in continuous speech recognition tasks. The corresponding covariance matrices that were subjected to the transform were clustered using phonetic knowledge. The impact of this approach was tested on the ASRs with PLP- and MFCC-based front-ends and was compared with other known feature decorrelation and dimension reduction techniques.