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Machine learning methodologies can be adopted in cultural applications andpropose new ways to distribute or even present the cultural content to thepublic. For instance, speech analytics can be adopted to automatically generatesubtitles in theatrical plays, in order to (among other purposes) help peoplewith hearing loss. Apart from a typical speech-to-text transcription withAutomatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can beused to automatically predict the underlying emotional content of speechdialogues in theatrical plays, and thus to provide a deeper understanding howthe actors utter their lines. However, real-world datasets from theatricalplays are not available in the literature. In this work we present GreThE, theGreek Theatrical Emotion dataset, a new publicly available data collection forspeech emotion recognition in Greek theatrical plays. The dataset containsutterances from various actors and plays, along with respective valence andarousal annotations. Towards this end, multiple annotators have been asked toprovide their input for each speech recording and inter-annotator agreement istaken into account in the final ground truth generation. In addition, wediscuss the results of some indicative experiments that have been conductedwith machine and deep learning frameworks, using the dataset, along with somewidely used databases in the field of speech emotion recognition.

A Dataset for Speech Emotion Recognition in Greek Theatrical Plays