The INTERSPEECH 2016 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: classification of deceptive vs. non-deceptive speech, the estimation of the degree of sincerity, and the identification of the native language out of eleven L1 classes of English L2 speakers. In this paper, we describe these sub-challenges, their conditions, the baseline feature extraction and classifiers, and the resulting baselines, as provided to the participants.

In this paper, we propose a method for automatically detecting various types of snore sounds using image classification convolutional neural network (CNN) descriptors extracted from audio file spectrograms. The descriptors, denoted as deep spectrum features, are derived from forwarding spectrograms through very deep task-independent pre-trained CNNs. Specifically, activations of fully connected layers from two common image classification CNNs, AlexNet and VGG19, are used as feature vectors. Moreover, we investigate the impact of differing spectrogram colour maps and two CNN architectures on the performance of the system. Results presented indicate that deep spectrum features extracted from the activations of the second fully connected layer of AlexNet using a viridis colour map are well suited to the task. This feature space, when combined with a support vector classifier, outperforms the more conventional knowledge-based features of 6 373 acoustic functionals used in the INTERSPEECH ComParE 2017 Snoring sub-challenge baseline system. In comparison to the baseline, unweighted average recall is increased from 40.6 % to 44.8 % on the development partition, and from 58.5 % to 67.0 % on the test partition.

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.

The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation Sub-Challenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.