CallSurf - Automatic transcription, indexing and structuration of call center conversational speech for knowledge extraction and query by content

Autor: Martine Garnier-Rizet, Gilles Adda, Frederik Cailliau, Jean-Luc Gauvain, Sylvie Guillemin-Lanne, Lori Lamel, Stephan Vanni, Claire Waast-Richard
Přispěvatelé: Cailliau, Frederik, VECSYS, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11), Laboratoire d'Informatique de Paris-Nord (LIPN), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS), Sinequa, Temis, EDF (EDF)
Jazyk: angličtina
Rok vydání: 2008
Předmět:
Zdroj: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
Sixth International Conference on Language Resources and Evaluation (LREC'08)
Sixth International Conference on Language Resources and Evaluation (LREC'08), May 2008, Marrakech, Morocco. pp.2623-2628
HAL
Popis: International audience; Being the client's first interface, call centres worldwide contain a huge amount of information of all kind under the form of conversational speech. If accessible, this information can be used to detect eg. major events and organizational flaws, improve customer relations and marketing strategies. An efficient way to exploit the unstructured data of telephone calls is data-mining, but current techniques apply on text only. The CALLSURF project gathers a number of academic and industrial partners covering the complete platform, from automatic transcription to information retrieval and data mining. This paper concentrates on the speech recognition module as it discusses the collection, the manual transcription of the training corpus and the techniques used to build the language model. The NLP techniques used to pre-process the transcribed corpus for data mining are POS tagging, lemmatization, noun group and named entity recognition. Some of them have been especially adapted to the conversational speech characteristics. POS tagging and preliminary data mining results obtained on the manually transcribed corpus are briefly discussed.
Databáze: OpenAIRE
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