Cita:
Advances in Nonlinear Speech Processing, International Conference on Non-Linear Speech Processing, NOLISP 2007 Paris, France, May 22-25, 2007 Revised Selected Papers. Springer, 2007. ISBN 978-3-540-77346-7. PP. 152-160
ISBN:
978-3-540-77346-7
ISSN:
0302-9743 (Print) 1611-3349 (Online)
DOI:
10.1007/978-3-540-77347-4_12
Revisado:
PeerReviewed
Serie/Num.:
Lecture Notes on Computer Science Volume 4885/2007
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 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.[+][-]
