The monitoring of an escalating negative interaction has several benefits, particularly in security, (mental) health, and group management. The speech signal is particularly suited to this, as aspects of escalation, including emotional arousal, are proven to easily be captured by the audio signal. A challenge of applying trained systems in real-life applications is their strong dependence on the training material and limited generalization abilities. For this reason, in this contribution, we perform an extensive analysis of three corpora in the Dutch language. All three corpora are high in escalation behavior content and are annotated on alternative dimensions related to escalation. A process of label mapping resulted in two possible ground truth estimations for the three datasets as low, medium, and high escalation levels. To observe class behavior and inter-corpus differences more closely, we perform acoustic analysis of the audio samples, finding that derived labels perform similarly across each corpus, with escalation interaction increasing in pitch (F0) and intensity (dB). We explore the suitability of different speech features, data augmentation, merging corpora for training, and testing on actor and non-actor speech through our experiments. We find that the extent to which merging corpora is successful depends greatly on the similarities between label definitions before label mapping. Finally, we see that the escalation recognition task can be performed in a cross-corpus setup with hand-crafted speech features, obtaining up to 63.8% unweighted average recall (UAR) at best for a cross-corpus analysis, an increase from the inter-corpus results of 59.4% UAR.@en

Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition, as well as emotion-target engagement and trustworthiness detection by means of more comprehensively integrating the audio-visual and language modalities. The purpose of MuSe 2020 is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We present three distinct sub-challenges: MuSe-Wild, which focuses on continuous emotion (arousal and valence) prediction; MuSe-Topic, in which participants recognise 10 domain-specific topics as the target of 3-class (low, medium, high) emotions; and MuSe-Trust, in which the novel aspect of trustworthiness is to be predicted. In this paper, we provide detailed information on MuSe-CAR, the first of its kind in-the-wild database, which is utilised for the challenge, as well as the state-of-the-art features and modelling approaches applied. For each sub-challenge, a competitive baseline for participants is set; namely, on test we report for MuSe-Wild a combined (valence and arousal) CCC of .2568, for MuSe-Topic a score (computed as 0.34∗UAR + 0.66∗F1) of 76.78 % on the 10-class topic and 40.64 % on the 3-class emotion prediction, and for MuSe-Trust a CCC of .4359.

@en