Published March 27, 2023 | Version v1
Dataset Restricted

ComParE 2023 HealthCall Corpus (HC-C)

Authors/Creators

  • 1. University of Augsburg, Germany
  • 2. Sorbonne University, France

Description

This is a subset (audio-only) of the HealthCall corpus, provided by Claude Montacié and colleagues. It is based on real audio interactions between call centre agents and customers who call to solve a problem or to request information. This corpus is designed to study natural spoken conversations and to predict Customer Relationship Management (CRM) annotations made by human agents from various vocal interaction, audio, and linguistic features. The corpus consists of 13,409 chunks of spoken conversations, each lasting 30 seconds. Each conversation was recorded on two separate and distinct audio channels: the first channel corresponds to the customer’s audio, and the second corresponds to the agent’s audio. More information can be found here.

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This data set is available to participants of the ComParE 2023 challenge (co-located with ACM Multimedia 2023 in Ottawa, Canada). Please see here for all information on the registration for ComParE 2023 and the EULA for the HC-C.

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Additional details

References

  • Björn W. Schuller, Anton Batliner, Shahin Amiriparian, Alexander Barnhill, Maurice Gerczuk, Andreas Triantafyllopoulos, Alice Baird, Panagiotis Tzirakis, Chris Gagne, Alan S. Cowen, Nikola Lackovic, Marie-José Caraty, Claude Montacié: The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests, Proceedings ACM Multimedia 2023, ACM, Vancouver, Canada, 2023.
  • Nikola Lackovic, Claude Montacié, Gauthier Lalande, Marie-José Caraty: Prediction of User Request and Complaint in Spoken Customer-Agent Conversations. https://arxiv.org/abs/2208.10249