Revision with unchanged content. In this book, a general framework for
feature enhancement is proposed which uses a multilayer perceptron (MLP)
to achieve optimal speaker dis-crimination. First, to train this MLP a
subset of speakers (speaker basis) is used to represent the underlying
characteristics of the given acoustic feature space. Second, the size of
the speaker basis is found to be among the crucial factors affecting the
performance of a speaker recognition system. Third, it is found that the
selection of the speaker basis can also influence system performance.
Based on this observation, an automatic speaker selection approach is
proposed on the basis of the maximal average between-class variance.
Tests in a variety of conditions, including clean and noisy as well as
telephone speech, show that this approach can improve the performance of
speaker recognition systems. Further, an alternative feature
representation is proposed in this book, which is derived from what we
call speaker voice signatures (SVS). These are trajectories in a Kohonen
self organising map (SOM) which has been trained to represent the
acoustic space. This feature representation is found to be complementary
to the baseline feature set. Finally, this book finishes with a number
of potential extensions of the pro-posed approaches. This book is
addressed to professionals in speech processing and students in this
domain.
