Support Vector Machines for continuous speech recognition

Although Support Vector Machines (SVMs) have been proved to be very powerful classifiers, they still have some problems which make difficult their application to speech recognition, and most of the tries to do it are combined HMM-SVM solutions. In this paper we show a pure SVM-based continuous speech recognizer, using the SVM to make decisions at frame-level, and a Token Passing algorithm to obtain the chain of recognized words. We consider a connected digit recognition task with both, digits themselves and number of digits, unknown. The experimental results show that, although not yet practical due to computational cost, such a system can get better recognition rates than traditional HMM-based systems (96.96% vs. 96.47%). To overcome computational problems, some techniques as the Mega-GSVCs can be used in the future.