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Abstract:
An important issue in applying SVMs to speech recognition is
the ability to classify variable length sequences. This paper
presents extensions to a standard scheme for handling this
variable length data, the Fisher score. A more useful mapping is
introduced based on the likelihood-ratio. The score-space defined
by this mapping avoids some limitations of the Fisher score.
Class-conditional generative models are directly incorporated
into the definition of the score-space. The mapping, and
appropriate normalisation schemes, are evaluated on a
speaker-independent isolated letter task where the new mapping
outperforms both the Fisher score and HMMs trained to maximise
likelihood.
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