Support vector machine with dynamic time-alignment kernel for speech recognition
نویسندگان
چکیده
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 speakerdependent 6 voiced-consonants recognition demonstrated excellent recognition performance of more than 98% in correct classification rate, whereas 93% by hidden Markov models (HMMs).
منابع مشابه
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...
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