Abstract

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.