  We propose a novel explainable machine learning (ML) model that identifies
depression from speech, by modeling the temporal dependencies across utterances
and utilizing the spectrotemporal information at the vowel level. Our method
first models the variable-length utterances at the local-level into a
fixed-size vowel-based embedding using a convolutional neural network with a
spatial pyramid pooling layer ("vowel CNN"). Following that, the depression is
classified at the global-level from a group of vowel CNN embeddings that serve
as the input of another 1D CNN ("depression CNN"). Different data augmentation
methods are designed for both the training of vowel CNN and depression CNN. We
investigate the performance of the proposed system at various temporal
granularities when modeling short, medium, and long analysis windows,
corresponding to 10, 21, and 42 utterances, respectively. The proposed method
reaches comparable performance with previous state-of-the-art approaches and
depicts explainable properties with respect to the depression outcome. The
findings from this work may benefit clinicians by providing additional
intuitions during joint human-ML decision-making tasks.
