  Researchers have recently started to study how the emotional speech heard by
young infants can affect their developmental outcomes. As a part of this
research, hundreds of hours of daylong recordings from preterm infants' audio
environments were collected from two hospitals in Finland and Estonia in the
context of so-called APPLE study. In order to analyze the emotional content of
speech in such a massive dataset, an automatic speech emotion recognition (SER)
system is required. However, there are no emotion labels or existing indomain
SER systems to be used for this purpose. In this paper, we introduce this
initially unannotated large-scale real-world audio dataset and describe the
development of a functional SER system for the Finnish subset of the data. We
explore the effectiveness of alternative state-of-the-art techniques to deploy
a SER system to a new domain, comparing cross-corpus generalization, WGAN-based
domain adaptation, and active learning in the task. As a result, we show that
the best-performing models are able to achieve a classification performance of
73.4% unweighted average recall (UAR) and 73.2% UAR for a binary classification
for valence and arousal, respectively. The results also show that active
learning achieves the most consistent performance compared to the two
alternatives.
