Very fast adaptation with a compact context-dependent eigenvoice model

The “eigenvoice” technique achieves rapid speaker adaptation by employing prior knowledge of speaker space obtained from reference speakers to place strong constraints on the initial model for each new speaker [9,10]. It has recently been shown to yield very fast adaptation for a large-vocabulary system [3] ([5] modifies the technique in an interesting way). In this paper, we describe a new way of applying the eigenvoice technique to context-dependent acoustic modeling, called the “Eigencentroid plus Delta Trees” (EDT) model. Here, the context-dependent model is defined so that it consists of a speaker-dependent component with a small number of parameters linked to a speaker-independent component with far more parameters. The eigenvoice technique can then be applied to the speaker-dependent component alone to attain very fast adaptation of the entire context-dependent model (e.g., 10% relative reduction in error rate after 3 sentences). EDT requires only a small number of parameters to represent speaker space and works even if only a small amount of data is available per reference speaker (in contrast to the system described in [3]).