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).

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Hiroshi Shimodaira, K.-I. Noma, Mitsuru Nakai, Shigeki Sagayama
• Keywords: Kernel Function, NIPS 2001, NIPS 2007, Speech Recognition, Standard Svm Training