Linguistic tree based maximum likelihood model interpolation

In this paper, a speaker adaptation method is presented which computes the speaker adapted model by a weighted sum of a set of speaker dependent models. The set of weights are estimated to maximize the likelihood of the adaptation data. Then a linguistic tree is constructed to cluster the mean vectors. The means in the same linguistic class share the same weight set, while the means in different classes use different weight set to compute the adapted model. Experiments show that with as little as 1~3 sentences a significant performance improvement is obtained. As more adaptation data is available, further improvement can be obtained.