Speaker adaptation using maximum likelihood model interpolation

A speaker adaptation scheme named maximum likelihood model interpolation (MLMI) is proposed. The basic idea of MLMI is to compute the speaker adapted (SA) model of a test speaker by a linear convex combination of a set of speaker dependent (SD) models. Given a set of training speakers, we first calculate the corresponding SD models for each training speaker as well as the speaker-independent (SI) models. Then, the mean vector of the SA model is computed as the weighted sum of the set of the SD mean vectors, while the covariance matrix is the same as that of the SI model. An algorithm to estimate the weight parameters is given which maximizes the likelihood of the SA model given the adaptation data. Experiments show that 3 adaptation sentences can give a significant performance improvement. As the number of SD models increases, further improvement can be obtained