Speech mining in noisy audio message corpus

Within the framework of automatic analysis of spoken telephone surveys we propose a robust Speech Mining strategy that selects, from a large database of spoken messages, the ones likely to be correctly processed by the Automatic Speech Recognition and Classification processes. The problem considered in this paper is the analysis of messages uttered by the users of a telephone service in response to a recorded message that asks if a problem they had was satisfactorily solved. Very often in these cases, subjective information is combined with factual information. The purpose of this type of analysis is the extraction of the distribution of users opinions. Therefore it is very important to check the representativeness of the subset of messages kept by the rejection strategies. Several measures, based on the Kullback-Leibler divergence, are proposed in order to evaluate the correctness of the information extracted as well as its representativeness. Index Terms : Automatic Speech Recognition, Speech Understanding, Confidence Measures, Speech Mining.