Using Crowdsourcing for Labelling Emotional Speech Assets

Authors

Alexey Tarasov, Technological University DublinFollow
Sarah Jane Delany, Technological University DublinFollow
Charlie Cullen, Technological University DublinFollow

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Publication Details

W3C workshop on Emotion ML, Paris, France

Abstract

The success of supervised learning approaches for the classification of emotion in speech depends highly on the quality of the training data. The manual annotation of emotion speech assets is the primary way of gathering training data for emotional speech recognition. This position paper proposes the use of crowdsourcing for the rating of emotion speech assets. Recent developments in learning from crowdsourcing offer opportunities to determine accurate ratings for assets which have been annotated by large numbers of non-expert individuals. The challenges involved include identifying good annotators, determining consensus ratings and learning the bias of annotators.

DOI

https://doi.org/10.21427/D7RS4G

Recommended Citation

Tarasov, A., Cullen, C. & Delany, S. (2010) Using Crowdsourcing for labeling emotional speech assets. W3C workshop on Emotion ML, Paris, France, 5-6 October. doi:10.21427/D7RS4G

Funder

Science Foundation Ireland