Expressive Probability Models for Speech Recognition and Understanding
نویسنده
چکیده
The paper is a brief summary of an invited talk given at the ASRU 99 conference. The principal points are as follows: first, that the expressive power of the probabilitymodels available for use in speech recognition and understanding has expanded significantly; second, that using expressive models such as dynamic Bayesian networks can result in improved learning rates and recognition results; and finally that still further expansion is required to tackle many problems of interest in the area of speech understanding. This further expansion should combine probability theory with the expressive power of first-order logical languages. The paper sketches an approximate inference method for representation systems of this kind.
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