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NIPS
2001

Dynamic Time-Alignment Kernel in Support Vector Machine

9 years 11 months ago
Dynamic Time-Alignment Kernel in Support Vector Machine
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).
Hiroshi Shimodaira, K.-I. Noma, Mitsuru Nakai, Shi
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where NIPS
Authors Hiroshi Shimodaira, K.-I. Noma, Mitsuru Nakai, Shigeki Sagayama
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