An Overview and Discussion of the Suitability of Existing Speech Datasets to Train Machine Learning Models for Collective Problem Solving
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Abstract (arXiv)
This report characterized the suitability of existing datasets for devising new Machine Learning models, decision making methods, and analysis algorithms to improve Collaborative Problem Solving and then enumerated requirements for future datasets to be devised. Problem solving was assumed to be performed in teams of about three, four members, which talked to each other. A dataset consists of the speech recordings of such teams. The characterization methodology was based on metrics that capture cognitive, social, and emotional activities and situations. The report presented the analysis of a large group of datasets developed for Spoken Language Understanding, a research area with some similarity to Collaborative Problem Solving.
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References · 6
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 732
- 2018Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacescited by 377
- 2019Speech Model Pre-training for End-to-End Spoken Language Understandingcited by 193
- 2023MCD: A Model-Agnostic Counterfactual Search Method For Multi-modal Design Modificationscited by 5
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