Xiong Xiao, Jinyu Li, and et. al
In this paper, we propose a novel acoustic model adaptation method for noise robust speech recognition. Model combination is a com- mon way to adapt acoustic models to a target test environment. For example, the mean supervectors of the adapted model is obtained as a linear combination of mean supervectors of many pre-trained environment-dependent acoustic models. Usually, the combination weights are estimated using a maximum likelihood (ML) criterion and the weights are nonzero for all the mean supervectors. We pro- pose to estimate the weights by using Lasso (least absolute shrink- age and selection operator) which imposes an 𝐿1 regularization term in the weight estimation problem to shrink some weights to exactly zero. Our study shows that Lasso usually shrinks to zero the weights of those mean supervectors not relevant to the test environment. By removing some nonrelevant supervectors, the obtained mean super- vectors are found to be more robust against noise distortions. Ex- perimental results on Aurora-2 task show that the Lasso-based mean combination consistently outperforms ML-based combination.
|Published in||Proc. ICASSP|