A new class of Support Vector Machine (SVM) which is applicable to sequential-pattern recognition is developed by incorporating an idea of non-linear time alignment into the kernel. Since time-alignment operation of sequential pattern is embedded in the kernel evaluation, same algorithms with the original SVM for training and classification can be employed without modifications. Furthermore, frame-wise evaluation of kernel in the proposed SVM (DTAK-SVM) enables frame-synchronous recognition of sequential pattern, which is suitable for continuous speech recognition. Preliminary experiments of speaker-dependent 6 voiced-consonants recognition demonstrated excellent recognition performance of more than 98% in correct classification rate, whereas 93% by hidden Markov models (HMMs).
Cite as: Shimodaira, H., Noma, K.-i., Nakai, M., Sagayama, S. (2001) Support vector machine with dynamic time-alignment kernel for speech recognition. Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001), 1841-1844, doi: 10.21437/Eurospeech.2001-435
@inproceedings{shimodaira01_eurospeech,
author={Hiroshi Shimodaira and Ken-ichi Noma and Mitsuru Nakai and Shigeki Sagayama},
title={{Support vector machine with dynamic time-alignment kernel for speech recognition}},
year=2001,
booktitle={Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001)},
pages={1841--1844},
doi={10.21437/Eurospeech.2001-435}
}