Constrained MLE-based speaker adaptation with L1 regularization

Abstract

Maximum a posterior (MAP) adaptation is one of the popular and powerful methods for obtaining a speaker-specific acoustic model. Basically, MAP adaptation needs a data storage for speaker adaptive (SA) model as much as speaker independent (SI) model needs. Modern speech recognition systems have a huge number of parameters and deal with millions of users. To reduce the data storage for SA models, in this paper, we propose a constrained maximum likelihood estimation-based speaker adaptation with L1 regularization. By the proposed method, we can more efficiently perform the model adjustments for SA models without almost any loss of phone recognition performance than the conventional sparse MAP adaptation method.