2 research outputs found
Segmental Eigenvoice With Delicate Eigenspace for Improved Speaker Adaptation
Eigenvoice techniques have been proposed to provide
rapid speaker adaptation with very limited adaptation data,
but the performance may be saturated when more adaptation
data become available. This is because in these techniques an
eigenspace with reduced dimensionality is established by properly
utilizing the a priori knowledge from the large quantity of training
data. The reduced dimensionality of the eigenspace requires
less adaptation data to estimate the model parameters for the
new speaker, but also makes it less easy to obtain more precise
models with more adaptation data. In this paper, a new segmental
eigenvoice approach is proposed, in which the eigenspace can be
further segmented into N subeigenspaces by properly classifying
the model parameters into N clusters. These N subeigenspaces
can help to construct a more delicate eigenspace and more precise
models when more adaptation data are available. It will be shown
that there can be at least mixture-based, model-based and feature-
based segmental eigenvoice approaches. Not only improved
performance can be obtained, but these different approaches can
be properly integrated to offer better performance. Two further
approaches leading to improved segmental eigenvoice techniques
with even better performance are also proposed. The experiments
were performed with b