Reference Eigen-Environment and Speaker Weighting for Robust Speech Recognition

Abstract

In this paper a reference eigen-environment and speaker weighting (RESW) method is proposed for online HMM adaptation. RESW establishes multiple eigen-MLLR subspaces as the set of a priori knowledge according to certain affecting factors, such as noise type, SNR, male and female. It then projects an input test utterance simultaneously into the set of eigen-subspaces and optimally synthesizes out a set of suitable HMMs. The proposed RESW was evaluated on Aurora 2 multi- condition training task. Experimental results showed that average word error rate (WER) of 6.11% was achieved. RESW not only outperformed the multi-condition training baseline (Multi-Con., 13.72%) but also the blind ETSI advanced DSR front-end (ETSI-Adv., 8.65%) and the histogram equalization (HEQ, 8.66%) and the non-blind reference model weighting (RMW, 7.29%) and Eigen-MLLR (6.14%) approaches.