  The detection of shouted speech is crucial in audio surveillance and
monitoring. Although it is desirable for a security system to be able to
identify emergencies, existing corpora provide only a binary label (i.e.,
shouted or normal) for each speech sample, making it difficult to predict the
shout intensity. Furthermore, most corpora comprise only utterances typical of
hazardous situations, meaning that classifiers cannot learn to discriminate
such utterances from shouts typical of less hazardous situations, such as
cheers. Thus, this paper presents a novel research source, the RItsumeikan
Shout Corpus (RISC), which contains wide variety types of shouted speech
samples collected in recording experiments. Each shouted speech sample in RISC
has a shout type and is also assigned shout intensity ratings via a
crowdsourcing service. We also present a comprehensive performance comparison
among deep learning approaches for speech type classification tasks and a shout
intensity prediction task. The results show that feature learning based on the
spectral and cepstral domains achieves high performance, no matter which
network architecture is used. The results also demonstrate that shout type
classification and intensity prediction are still challenging tasks, and RISC
is expected to contribute to further development in this research area.
