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Abstract:
A new class of Support Vector Machine (SVM) that is applicable
to sequential-pattern recognition such as speech recognition is
developed by incorporating an idea of non-linear time alignment
into the kernel function. Since the time-alignment operation of
sequential pattern is embedded in the new kernel function,
standard SVM training and classification algorithms can be
employed without further modifications. The proposed SVM
(DTAK-SVM) is evaluated in speaker-dependent speech recognition
experiments of hand-segmented phoneme recognition. Preliminary
experimental results show comparable recognition performance with
hidden Markov models (HMMs).
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