Raw Audio for Depression Detection Can Be More Robust Against Gender Imbalance than Mel-Spectrogram Features

Depression is a large-scale mental health problem and a challenging area for machine learning researchers in terms of the detection of depression. Datasets such as the Distress Analysis Interview Corpus - Wizard of Oz have been created to aid research in this area. However, on top of the challenges inherent in accurately detecting depression, biases in datasets may result in skewed classification performance. In this paper we examine gender bias in the DAIC-WOZ dataset using audio-based deep neural networks. We show that gender biases in DAIC-WOZ can lead to an overreporting of performance, which has been overlooked in the past due to the same gender biases being present in the test set. By using raw audio and different concepts from Fair Machine Learning, such as data re-distribution, we can mitigate against the harmful effects of bias.