Dynamic Time-Alignment Kernel in Support Vector Machine
Abstract
A new class of Support Vector Machine (SVM) that is applica- ble 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, stan- dard 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 experimen- tal results show comparable recognition performance with hidden Markov models (HMMs). 1 Introduction
Cite
Text
Shimodaira et al. "Dynamic Time-Alignment Kernel in Support Vector Machine." Neural Information Processing Systems, 2001.Markdown
[Shimodaira et al. "Dynamic Time-Alignment Kernel in Support Vector Machine." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/shimodaira2001neurips-dynamic/)BibTeX
@inproceedings{shimodaira2001neurips-dynamic,
title = {{Dynamic Time-Alignment Kernel in Support Vector Machine}},
author = {Shimodaira, Hiroshi and Noma, Ken-ichi and Nakai, Mitsuru and Sagayama, Shigeki},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {921-928},
url = {https://mlanthology.org/neurips/2001/shimodaira2001neurips-dynamic/}
}