ISCA Archive SMM 2023
ISCA Archive SMM 2023

Teamwork Quality Prediction Using Speech-Based Features

Martin Meza, Lara Gauder, Lautaro Estienne, Ricardo Barchi, Agustı́n Gravano, Pablo Riera, Luciana Ferrer

This paper describes a novel protocol for annotating teamwork quality and related variables, based only on the speech signal. Our protocol was designed to annotate a Spanish version of the Objects Games corpus, a publicly available corpus that contains dialogues of people playing a collaborative computer game. The corpus was annotated by 4 raters, who achieved an Intra class Correlation Coefficient of 0.64 for the main teamwork quality metric. Using the resulting annotations, we developed a system for automatic prediction of the average teamwork quality across raters using features extracted from the conversations, reaching a coefficient of determination, R2 of 0.56. This result suggests that automatic prediction of teamwork quality from the speech signal of the teammates is a feasible task.

doi: 10.21437/SMM.2023-1

Cite as: Meza, M., Gauder, L., Estienne, L., Barchi, R., Gravano, A., Riera, P., Ferrer, L. (2023) Teamwork Quality Prediction Using Speech-Based Features. Proc. SMM23, Workshop on Speech, Music and Mind 2023, 1-5, doi: 10.21437/SMM.2023-1

  author={Martin Meza and Lara Gauder and Lautaro Estienne and Ricardo Barchi and Agustı́n Gravano and Pablo Riera and Luciana Ferrer},
  title={{Teamwork Quality Prediction Using Speech-Based Features}},
  booktitle={Proc. SMM23, Workshop on Speech, Music and Mind 2023},