Improved Bases Selection in Acoustic Model Interpolation for Fast On-Line Adaptation

Abstract

This work presents a novel bases selection approach for acoustic model interpolation based fast on-line adaptation. The proposed approach employs a correlation based similarity measure in the supervector domain (derived by concatenating the Gaussian mean parameters of the adapted models) for the selection of bases. This approach is found to greatly reduce the computational complexity in comparison to the Viterbi-alignment based bases search. Moreover, the proposed approach employs joint representation along with orthogonalization for the dynamic selection of bases. Consequently, the selected bases result in a much balanced coverage of phonetic contexts in the synthesized adapted model. The proposed technique is found to result in improved performance for all three modes of adaptation viz. the utterance-specific, the incremental and the batch modes. For utterance-specific mode, it achieves a relative improvement of 10.2% over baseline with only 3 to 5 seconds of adaptation data.