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Researchers have recently started to study how the emotional speech heard byyoung infants can affect their developmental outcomes. As a part of thisresearch, hundreds of hours of daylong recordings from preterm infants' audioenvironments were collected from two hospitals in Finland and Estonia in thecontext of so-called APPLE study. In order to analyze the emotional content ofspeech in such a massive dataset, an automatic speech emotion recognition (SER)system is required. However, there are no emotion labels or existing indomainSER systems to be used for this purpose. In this paper, we introduce thisinitially unannotated large-scale real-world audio dataset and describe thedevelopment of a functional SER system for the Finnish subset of the data. Weexplore the effectiveness of alternative state-of-the-art techniques to deploya SER system to a new domain, comparing cross-corpus generalization, WGAN-baseddomain adaptation, and active learning in the task. As a result, we show thatthe best-performing models are able to achieve a classification performance of73.4% unweighted average recall (UAR) and 73.2% UAR for a binary classificationfor valence and arousal, respectively. The results also show that activelearning achieves the most consistent performance compared to the twoalternatives.

Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit