4.5 Review

Affective and behavioural computing: Lessons learnt from the First Computational Paralinguistics Challenge

Journal

COMPUTER SPEECH AND LANGUAGE
Volume 53, Issue -, Pages 156-180

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.csl.2018.02.004

Keywords

Computational Paralinguistics; Social Signals; Conflict; Emotion; Autism; Survey; Challenge

Funding

  1. European Community's Seventh Framework Programme [ASC-Inclusion] [289021]
  2. European Union's Framework Programme for Research and Innovation HORIZON 2020 [ARIA-VALUSPA] [645378]
  3. European Union Seventh Framework Programme ERC Starting Grant [iHEARu] [338164]
  4. Laboratory of Excellence SMART [ANR-11-LABX-65]
  5. French State funds [ANR-11-IDEX-0004-02]
  6. Association for the Advancement of Affective Computing
  7. Social Signal Processing Network (SSPNet)
  8. European Research Council (ERC) [338164] Funding Source: European Research Council (ERC)

Ask authors/readers for more resources

In this article, we review the INTERSPEECH 2013 Computational Paralinguistics ChallengE (ComParE) - the first of its kind in light of the recent developments in affective and behavioural computing. The impact of the first ComParE instalment is manifold: first, it featured various new recognition tasks including social signals such as laughter and fillers, conflict in dyadic group discussions, and atypical communication due to pervasive developmental disorders, as well as enacted emotion; second, it marked the onset of the ComParE, subsuming all tasks investigated hitherto within the realm of computational paralinguistics; finally, besides providing a unified test-bed under well-defined and strictly comparable conditions, we present the definite feature vector used for computation of the baselines, thus laying the foundation for a successful series of follow-up Challenges. Starting with a review of the preceding INTERSPEECH Challenges, we present the four Sub-Challenges of ComParE 2013. In particular, we provide details of the Challenge databases and a meta-analysis by conducting experiments of logistic regression on single features and evaluating the performances achieved by the participants. (C) Crown Copyright 2018 Published by Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available