Speech Mining in Noisy Audio Message Corpus

Abstract : 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 .
Type de document :
Communication dans un congrès
Interspeech, Aug 2007, Anvers, Belgium
Liste complète des métadonnées

Contributeur : Bibliothèque Universitaire Déposants Hal-Avignon <>
Soumis le : mercredi 11 mai 2016 - 15:11:33
Dernière modification le : samedi 23 mars 2019 - 01:22:40


  • HAL Id : hal-01314518, version 1



Nathalie Camelin, Frédéric Béchet, Géraldine Damnati, Renato De Mori. Speech Mining in Noisy Audio Message Corpus. Interspeech, Aug 2007, Anvers, Belgium. 〈hal-01314518〉



Consultations de la notice