Acoustic model adaptation based on coarse/fine training of transfer vectors and its application to a speaker adaptation task

In this paper, we propose a novel adaptation technique based on coarse/fine training of transfer vectors. We focus on transfer vector estimation of a Gaussian mean from an initial model to an adapted model. The transfer vector is decomposed into a direction vector and a scaling factor. By using tied-Gaussian class (coarse class) estimation for the direction vector, and by using individual Gaussian class (fine class) estimation for the scaling factor, we can obtain accurate transfer vectors with a small number of parameters. Simple training algorithms for transfer vector estimation are analytically derived using the variational Bayes, maximum a posteriori (MAP) and maximum likelihood methods. Speaker adaptation experiments show that our proposals clearly improve speech recognition performance for any amount of adaptation data, compared with conventional MAP adaptation.