A Dataset for Speech Emotion Recognition in Greek Theatrical Plays
Machine learning methodologies can be adopted in cultural applications and
propose new ways to distribute or even present the cultural content to the
public. For instance, speech analytics can be adopted to automatically generate
subtitles in theatrical plays, in order to (among other purposes) help people
with hearing loss. Apart from a typical speech-to-text transcription with
Automatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can be
used to automatically predict the underlying emotional content of speech
dialogues in theatrical plays, and thus to provide a deeper understanding how
the actors utter their lines. However, real-world datasets from theatrical
plays are not available in the literature. In this work we present GreThE, the
Greek Theatrical Emotion dataset, a new publicly available data collection for
speech emotion recognition in Greek theatrical plays. The dataset contains
utterances from various actors and plays, along with respective valence and
arousal annotations. Towards this end, multiple annotators have been asked to
provide their input for each speech recording and inter-annotator agreement is
taken into account in the final ground truth generation. In addition, we
discuss the results of some indicative experiments that have been conducted
with machine and deep learning frameworks, using the dataset, along with some
widely used databases in the field of speech emotion recognition.