Online adaptation using speatransformation space model evolution

This paper presents a new approach to online speaker adaptation based on transformation space model evolution. This approach extends the previous idea of speaker space model evolution by applying the a priori knowledge of training speakers to the speaker-dependent maximum likelihood linear regression (MLLR) matrix parameters. A quasi-Bayes (QB) estimation algorithm is devised to incrementally update the hyperparameters of the transformation space model and the regression matrices simultaneously. Experiments on supervised speaker adaptation demonstrate that the proposed approach is more effective compared with the conventional quasi-Bayes linear regression (QBLR) technique when a small amount of adaptation data is available.