Improving online incremental speaker adaptation with eigen feature space MLLR

This paper investigates an eigen feature space maximum likelihood linear regression (fMLLR) scheme to improve the performance of online speaker adaptation in automatic speech recognition systems. In this stochastic-approximation-like framework, the traditional incremental fMLLR estimation is considered as a slowly changing mean of the eigen fMLLR. It helps the adaptation when only a limited amount of data is available at the beginning of the conversation. The scheme is shown to be able to balance the transformation estimation given the data and yields reasonable improvements for online systems.