Crowdsourcing Emotional Speech
Jennifer Smith, Andreas Tsiartas, Valerie Wagner, Elizabeth Shriberg, Nikoletta Bassiou
Abstract
We describe the methodology for the collection and annotation of a large corpus of emotional speech data through crowdsourcing. The corpus offers 187 hours of data from 2,965 subjects. Data includes non-emotional recordings from each subject as well as recordings for five emotions: angry, happy-low-arousal, happy-high-arousal, neutral, and sad. The data consist of spontaneous speech elicited from subjects via a web-based tool. Subjects used their own personal recording equipment, resulting in a data set that contains variation in room acoustics, microphone, etc. This offers the advantage of matching the type of variation one would expect to see when exposing speech technology in the wild in a web-based environment. The annotation scheme covers the quality of emotion expressed through the tone of voice and what was said, along with common audio-quality issues. We discuss lessons learned in the process of the creation of this corpus.
BibTeX
@inproceedings{icassp2018_crowdsourcingemo,
title = {Crowdsourcing Emotional Speech},
author = {Jennifer Smith and Andreas Tsiartas and Valerie Wagner and Elizabeth Shriberg and Nikoletta Bassiou},
booktitle = {ICASSP 2018},
year = {2018}
}