Segmental Eigenvoice With Delicate Eigenspace for Improved Speaker Adaptation
Journal
IEEE Transactions on Speech and Audio Processing
Journal Volume
13
Journal Issue
3
Pages
399-411
Date Issued
2005-05
Date
2005-05
Author(s)
DOI
246246/200611150121882
Abstract
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 bo
Subjects
Eigenvector approach
principal component analysis
speaker adaptation
SDGs
Publisher
Taipei:National Taiwan University Dept Elect Engn
Type
journal article
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