SVM CLASSIFIERS FOR ASR: A DISCUSSION ABOUT PARAMETERIZATION (FriAmSS2)
Author(s) :
Jose Miguel Garcia-Cabellos (Universidad Carlos III de Madrid, Spain)
Carmen Pelaez-Moreno (Universidad Carlos III de Madrid, Spain)
Ascension Gallardo-Antolin (Universidad Carlos III de Madrid, Spain)
Fernando Perez-Cruz (Universidad Carlos III de Madrid, Spain)
Fernando Diaz-de-Maria (Universidad Carlos III de Madrid, Spain)
Abstract : Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers could provide an appropriate solution, since they are very well adapted to high-dimension classification problems. Nevertheless, the use of SVMs for ASR is by no means straightforward, because SVM classifiers require a fixed-dimension input. In this paper we propose and compare three alternatives for adapting the parameterization to the fixed-input dimension required by SVMs. We show that SVM classifiers outperforms the conventional HMM-based ASR system, when the speech signal is parameterised at properly selected instants.

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