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Automatic customer feedback processing: alarm detection in open question spoken messages

Abstract : This paper describes an alarm detection system dedicated to process automatically customer feedbacks in call-centers. Previous studies presented a strategy that consists in the robust detection of subjective opinions about a particular topic in a spoken message. In the present study, we focus on the alarm detection problem in a customer spoken feedback application. We want to characterize each customer's survey with a degree of emergency. All the messages considered as urgent need a quick answer from the call-center service in order to satisfy the customer. The strategy proposed is based on a classification scheme that takes into account all the features that can characterize a survey: answers to the closed questions, topics and opinions detected in the open question spoken message, confidence scores from the Automatic Speech Recognition (ASR) and Spoken Language Understanding (SLU) modules. A field trial realized among real customers has shown that despite the ASR robustness issues, our system efficiently ranks the most urgent messages and brings a finer analysis on the surveys than the one provided by processing the closed questions alone.
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https://hal.archives-ouvertes.fr/hal-01314565
Contributor : Bibliothèque Universitaire Déposants Hal-Avignon <>
Submitted on : Wednesday, May 11, 2016 - 3:48:06 PM
Last modification on : Friday, March 6, 2020 - 2:39:50 PM

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  • HAL Id : hal-01314565, version 1

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Nathalie Camelin, Geraldine Damnati, Frederic Bechet, Renato de Mori. Automatic customer feedback processing: alarm detection in open question spoken messages. Interspeech, Sep 2008, brisbane, Australia. ⟨hal-01314565⟩

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