Machine learning for identifying how much women and men talk in meetings

Abstract: For quite some time, it has been discussed that women are underrepresented in company boards. Furthermore, when they are a member of a board, they tend to have lower positions than men, meaning they have less power. One way to start solving the problem is to have more women in company boards and ensure they too have high positions. However, only having more women present might not be a complete solution. They also need space to speak to share their competence, ideas, and thoughts. Although, people tend to perceive women as more talkative than they actually are. For example, if a woman and a man speak the same amount of time, the woman is often perceived as having talked more than the man. To identify this problem, this study aimed to train a machine learning model that takes a recording of a meeting as input and calculates the time women and men spoke in percentage. The training data was based on 1266 episodes from the radio show “Sommar och Vinter i P1” where all episodes contained one speaker, different each time. 633 episodes contained female speakers and 633 contained male speakers, all speakers spoke Swedish in the recordings. Four different models were trained using different training data, where logistic regression is the best performing algorithm for all four. The four models were evaluated using evaluation data and they showed to not differ significantly in performance. The subsequently chosen model was tested on two recordings with both male and female speakers, where the resulting accuracy was 83.5% and 83.1%. The application developed in this study can help identify the speaking space given to women in the workplace. However, how this tool could be used to achieve a more equal workplace still needs further research.