A support vector machines-based rejection technique for speechrecognition
Changxue Ma; Randolph, M.A.; Drish, J.
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP apos;01). 2001 IEEE International Conference on
Volume 1, Issue , 2001 Page(s):381 - 384 vol.1
Digital Object Identifier 10.1109/ICASSP.2001.940847
Summary:Support vector machines represent a new approach to pattern
classification developed from the theory of structural risk
minimization. In this paper, we present an investigation into the
application of support vector machines to the confidence measurement
problem in speech recognition. Specifically, based on the results from
an initial decoding of an utterance during speech recognition, we derive
a feature vector consisting of parameters such as word score density,
N-best word score density differences, relative word score and relative
word duration as input to the confidence measurement process in which
hypothetically correct utterances are accepted and utterances determined
to be incorrect are rejected. We propose a new approach to training
support vector machines. In this paper, we train and test a support
vector machines classifier and compare the results with other
statistical classification methods
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