Gender Bias in Machine Learning : The Effect of Using Female Versus Male Audio When Classifying Emotions in Speech Using Machine Learning

Abstract: To avoid discrimination between the genders and to improve the performance of machine learning, it is important to evaluate how different test data can impact how accurate machine learning models can be. This study investigates if the distribution between women and men in the training data affects how accurately different machine learning models can classify emotions used in the speaker’s tone of voice. The data used in the study is the RAVDESS dataset, where a part of the data was used for the training and the rest was used for testing the accuracy of the machine learning models. When analyzing the results, it was found that when comparing the results of using 75 % female and 25 % male, 25 % female and 75 % male or equal parts male and female training data, the highest accuracy of the majority of the models was when using equal parts male and female test data. Comparing the result of using 75 % female and 25 % male versus 25 % female and 75 % male, the accuracy was slightly higher when using a majority of the data being male. However, the difference was so small that no conclusion could be drawn more than that using equal parts training data from both genders is preferable.