MAP adaptation with subspace regression classes and tying

In the hidden Markov modeling framework with mixture Gaussians, adaptation is often done by modifying the Gaussian mean vectors using MAP estimation or MLLR transformation. When the amount of adaptation data is scarce or when some speech units are unseen in the data, it is necessary to do adaptation in groups-either with regression classes of Gaussians or via vector field smoothing. In this paper, we propose to derive regression classes of subspace Gaussians for MAP adaptation. The motivation is that clustering at the finer acoustic level of subspace Gaussians of lower dimension is more effective, resulting in lower distortions and relatively fewer regression classes. Experiments in which context-dependent TIMIT HMMs are adapted to the resource management task with few minutes of speech show improvement of our subspace regression classes over traditional full-space regression classes