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Methods of Combining Multiple Classifiers with Different Features
and Their Applications to Text-Independent Speaker Identification
ABSTRACT
In practical applications of pattern recognition, there are often
different features extracted from raw data which needs recognizing.
Methods of combining multiple classifiers
with different features are viewed as a general problem in various
application areas of pattern recognition. In this paper, a systematic
investigation has been made and possible solutions are classified into
three frameworks, i.e. linear opinion pools, winner-take-all and
evidential reasoning. For combining multiple classifiers
with different features, a novel method is presented in the framework
of linear opinion pools and a modified training algorithm for associative
switch is also proposed in the framework of winner-take-all.
In the framework of evidential reasoning, several typical methods are
briefly reviewed for use. All aforementioned methods have already been
applied to text-independent speaker identification. The simulations show
that results yielded by the methods described in this paper are better
than not only the individual classifiers' but also ones obtained by combining
multiple classifiers with the same feature. It indicates that the use of
combining multiple classifiers with different features is an effective way
to attack the problem of text-independent speaker identification.
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