ISCA Archive Eurospeech 2001
ISCA Archive Eurospeech 2001

Support vector machine with dynamic time-alignment kernel for speech recognition

Hiroshi Shimodaira, Ken-ichi Noma, Mitsuru Nakai, Shigeki Sagayama

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).


doi: 10.21437/Eurospeech.2001-435

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}
}