The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

Schuller, Björn; Steidl, Stefan; Batliner, Anton; Marschik, Peter B.; Baumeister, Harald; Dong, Fengquan; Hantke, Simone; Pokorny, Florian B.; Rathner, Eva-Maria; Bartl-Pokorny, Katrin D.; Einspieler, Christa; Zhang, Dajie; Baird, Alice; Amiriparian, Shahin; Qian, Kun; Ren, Zhao; Schmitt, Maximilian; Tzirakis, Panagiotis; Zafeiriou, Stefanos

doi:10.21437/Interspeech.2018-51

The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

Björn Schuller, Stefan Steidl, Anton Batliner, Peter B. Marschik, Harald Baumeister, Fengquan Dong, Simone Hantke, Florian B. Pokorny, Eva-Maria Rathner, Katrin D. Bartl-Pokorny, Christa Einspieler, Dajie Zhang, Alice Baird, Shahin Amiriparian, Kun Qian, Zhao Ren, Maximilian Schmitt, Panagiotis Tzirakis, Stefanos Zafeiriou

The INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified; in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem; in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart; and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the ‘usual’ ComParE and BoAW features and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series.