Jacobian adaptation based on the frequency-filtered spectral energies

Abstract

Jacobian Adaptation (JA) of the acoustic models is an efficient adaptation technique for robust speech recognition. Several improvements for the JA have been proposed in the last years, either to generalize the Jacobian linear transformation for the case of large noise mismatch between training and testing or to extend the adaptation to other degrading factors, like channel distortion and vocal tract length. However, the JA technique has only been used so far with the conventional mel-frequency cepstral coefficients (MFCC). In this paper, the JA technique is applied to an alternative type of features, the Frequency-Filtered (FF) spectral energies, resulting in a more computationally efficient approach. Furthermore, in experimental tests with the database Aurora1, this new approach has shown an improved recognition performance with respect to the Jacobian adaptation with MFCCs.