ISCA Archive Interspeech 2007
ISCA Archive Interspeech 2007

Speech mining in noisy audio message corpus

Nathalie Camelin, Frédéric Béchet, Géraldine Damnati, Renato De Mori

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.

doi: 10.21437/Interspeech.2007-184

Cite as: Camelin, N., Béchet, F., Damnati, G., Mori, R.D. (2007) Speech mining in noisy audio message corpus. Proc. Interspeech 2007, 2401-2404, doi: 10.21437/Interspeech.2007-184

  author={Nathalie Camelin and Frédéric Béchet and Géraldine Damnati and Renato De Mori},
  title={{Speech mining in noisy audio message corpus}},
  booktitle={Proc. Interspeech 2007},