Abramov Simon, Korotin Alexander, Somov Andrey, Burnaev Evgeny, Stepanov Anton, Nikolaev Dmitry, Titova Maria A
IEEE J Biomed Health Inform. 2022 Aug;26(8):3597-3606. doi: 10.1109/JBHI.2021.3119202. Epub 2022 Aug 11.
Video gaming and eSports is a quickly developing industry already involving billions of players worldwide. Gaming and eSports tournaments require strong mental abilities to avoid severe stress and other negative consequences upon completing the game. In this article, we report on the impact of emotions on a team performance. For this reason, we collect audio recordings and game logs from the players in real conditions at an eSports tournament. This data is further used in trained machine learning models for analysis of players' emotional conditions from the voice during the game. We considered recognition of several types of emotions as well as the background sounds. To do this, we trained 92.7% accuracy classifier of six most common classes of emotions and sounds in eSports audio and applied it to eSports data. As a result, we demonstrate that there is an opportunity to measure the eSports team's performance from the players' emotional conditions obtained from the voice communication. We found that there is a strong correlation among the performance of the team, communication between the players, and emotional sentiment of communication. The teams achieve much better results when they had much more internal conversations during the game.
电子游戏和电子竞技是一个迅速发展的行业，已经涉及全球数十亿玩家。电子游戏和电子竞技比赛需要强大的心理能力，以避免在完成游戏时产生严重压力和其他负面后果。在本文中，我们报告了情绪对团队表现的影响。因此，我们在一场电子竞技比赛的真实环境中收集了玩家的音频记录和游戏日志。这些数据进一步用于经过训练的机器学习模型，以从游戏期间的语音分析玩家的情绪状况。我们考虑了几种情绪类型以及背景声音的识别。为此，我们训练了一个在电子竞技音频中六种最常见的情绪和声音类别上准确率为92.7%的分类器，并将其应用于电子竞技数据。结果，我们证明有机会从通过语音通信获得的玩家情绪状况来衡量电子竞技团队的表现。我们发现团队表现、玩家之间的沟通以及沟通的情感倾向之间存在很强的相关性。当团队在游戏中有更多内部对话时，他们会取得更好的成绩。
