A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Hybrid HMM/ANN Systems for Speech Recognition: Overview and New Research Directions
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Robust ASR using Support Vector Machines
Speech Communication
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Support Vector Machines (SVMs) are state-of-the-art methods for machine learning but share with more classical Artificial Neural Networks (ANNs) the difficulty of their application to input patterns of non-fixed dimension. This is the case in Automatic Speech Recognition (ASR), in which the duration of the speech utterances is variable. In this paper we have recalled the hybrid (ANN/HMM) solutions provided in the past for ANNs and applied them to SVMs performing a comparison between them. We have experimentally assessed both hybrid systems with respect to the standard HMM-based ASR system, for several noisy environments. On the one hand, the ANN/HMM system provides better results than the HMM-based system. On the other, the results achieved by the SVM/HMM system are slightly lower than those of the HMM system. Nevertheless, such a results are encouraging due to the current limitations of the SVM/HMM system.