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Informatics Report Series
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| Title:Dynamic Time-Alignment Kernel in Support Vector Machine |
| Authors:
Hiroshi Shimodaira
; Ken-ichi Noma
; Mitsuru Nakai
; Shigeki Sagayama
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| Date:Dec 2002 |
| Publication Title:Advances in Neural Information Processing Systems (NIPS2002) |
| Publisher:MIT Press |
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Publication Type:Conference Paper
Publication Status:Published
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Volume No:14(2)
Page Nos:921-928
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ISBN/ISSN:0262042088
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- Abstract:
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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).
- Links To Paper
- 1st Link
- Bibtex format
- @InProceedings{EDI-INF-RR-0674,
- 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 = {http://www.cstr.ed.ac.uk/downloads/publications/2001/Shimodaira2001NIPS.pdf},
- }
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