Eigenvoices for speaker adaptation

We have devised a new class of fast adaptation techniques for speech recognition, based on prior knowledge of speaker variation. To obtain this prior knowledge, one applies Principal Component Analysis (PCA) [9] or a similar technique to a training set of T vectors of dimension D derived from T speaker-dependent (SD) models. This offline step yields T basis vectors, which we call “eigenvoices” by analogy with the eigenfaces employed in face recognition [14,18]. We constrain the model for new speaker S to be located in K-space, the space spanned by the first K eigenvoices. Speaker adaptation then involves estimating the K eigenvoice coefficients for the new speaker; typically, K is very small compared to the original dimension D.