SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults
This provides a domain-specific resource for researchers working on speech emotion recognition in older Italian populations, but it is incremental as it focuses on a new dataset rather than novel methods.
The authors tackled the lack of a speech emotion recognition dataset for Italian older adults by introducing SER_AMPEL, a multi-source dataset collected from acted and natural conversations, with preliminary classification results reported on a subset.
In this paper, SER_AMPEL, a multi-source dataset for speech emotion recognition (SER) is presented. The peculiarity of the dataset is that it is collected with the aim of providing a reference for speech emotion recognition in case of Italian older adults. The dataset is collected following different protocols, in particular considering acted conversations, extracted from movies and TV series, and recording natural conversations where the emotions are elicited by proper questions. The evidence of the need for such a dataset emerges from the analysis of the state of the art. Preliminary considerations on the critical issues of SER are reported analyzing the classification results on a subset of the proposed dataset.