Summary of An Overview and Discussion Of the Suitability Of Existing Speech Datasets to Train Machine Learning Models For Collective Problem Solving, by Gnaneswar Villuri et al.
An Overview and Discussion of the Suitability of Existing Speech Datasets to Train Machine Learning Models for Collective Problem Solving
by Gnaneswar Villuri, Alex Doboli
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the suitability of existing datasets for developing new Machine Learning models and decision-making methods that enhance Collaborative Problem Solving. They analyze a collection of speech recordings from teams of three to four members engaging in problem-solving activities. The characterization methodology relies on metrics capturing cognitive, social, and emotional aspects. By applying this framework to datasets from Spoken Language Understanding, the study aims to provide insights for future dataset development that can support Collaborative Problem Solving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to create better tools for teams working together to solve problems. Scientists recorded conversations of small groups talking about challenges and figured out what makes these conversations helpful or unhelpful. They looked at big collections of recordings from a related field, Spoken Language Understanding, to see if they can use similar methods to help teams solve problems. |
Keywords
» Artificial intelligence » Language understanding » Machine learning
