On combining confidence measures for improved rejection of incorrect data
نویسندگان
چکیده
In this paper, techniques for combining confidence measures are proposed and evaluated. Confidence measures are useful for rejecting incorrect data, which is an important issue in speech recognition based interactive systems. Many ways of computing individual confidence measures have already been investigated. A detailed analysis of various confidence measures shows that they behave differently for what concerns rejection of incorrect data on various field data subsets (substitution errors, out-of-vocabulary data & noise tokens) collected from a vocal directory task. Two combination methods are then presented. One combines confidence measures by means of a neural network and the other through logistic regression. Evaluations shows that both combination techniques are efficient, and both take the best of the various individual confidence measures involved on each data subset.
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