Eigenvoices : a compact representation of speakers in a model space

In this article, we present a new approach to modeling speaker-dependent systems. The approach was inspired by the eigenfaces techniques used in face recognition. We build a linear vector space of low dimensionality, called eigenspace, in which speakers are located. The basis vectors of this space are called eigenvoices. Each eigenvoice models a direction of inter-speaker variability. The eigenspace is built during the training phase. Then, any speaker model can be expressed as a linear combination of eigenvoices. The benefits of this technique as set forth in this article reside in the reduction of the number of parameters that describe a model. Thereby we are able to reduce the number of parameters to estimate, as well as computation and/or storage costs. We apply the approach to speaker adaptation and speaker recognition. Some experimental results are supplied.