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Abstract:
Measuring distance between variable-length sequences is a key
challenge that arises in text processing, for example, machine
translation, and in computational biology. To tackle such inherently
high dimension variables, kernel methods from statistical learning
theory provide an efficient computational tool. Rational kernels
provide an attractive solution for text processing. Speech processing
introduces additional challenge due to the inherent ambiguity in
speech input. In this talk, we will examine how rational kernels can
be extended to speech processing and used for combining different
input modalities such as visual cues. This general framework can be
applied to classify spoken utterances and will be illustrated using a
call-center application where a speaker's emotional expression or
affect is recognized from his/her voice.
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