Publication: A Speech Recognizer based on Multiclass SVMs with HMM-Guided Segmentation
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2006
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Springer-Verlag
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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-dimensional classification problems.
Nevertheless, the use of SVMs for ASR is by no means straightforward,
mainly because SVM classifiers require an input of fixed-dimension.
In this paper we study the use of a HMM-based segmentation as a mean to
get the fixed-dimension input vectors required by SVMs, in a problem of
isolated-digit recognition. Different configurations for all the parameters
involved have been tested. Also, we deal with the problem of multi-class
classification (as SVMs are initially binary classifers), studying two of the
most popular approaches: 1-vs-all and 1-vs-1.
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Keywords
Automatic Speech Recognition, Support vector machines
Bibliographic citation
Nonlinear Analyses and Algorithms for Speech Processing. International Conference on Non-Linear Speech Processing, NOLISP 2005, Barcelona, Spain, April 19-22, 2005, Revised Selected Papers. PP. 257-266