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Title:Dynamic Time-Alignment Kernel in Support Vector Machine
Authors: Hiroshi Shimodaira ; Ken-ichi Noma ; Mitsuru Nakai ; Shigeki Sagayama
Date:Dec 2002
Publication Title:Advances in Neural Information Processing Systems (NIPS2002)
Publisher:MIT Press
Publication Type:Conference Paper Publication Status:Published
Volume No:14(2) Page Nos:921-928
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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Bibtex format
author = { Hiroshi Shimodaira and Ken-ichi Noma and Mitsuru Nakai and Shigeki Sagayama },
title = {Dynamic Time-Alignment Kernel in Support Vector Machine},
book title = {Advances in Neural Information Processing Systems (NIPS2002)},
publisher = {MIT Press},
year = 2002,
month = {Dec},
volume = {14(2)},
pages = {921-928},
url = {},

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