  Psychomotor retardation associated with depression has been linked with
tangible differences in vowel production. This paper investigates a
knowledge-driven machine learning (ML) method that integrates spectrotemporal
information of speech at the vowel-level to identify the depression. Low-level
speech descriptors are learned by a convolutional neural network (CNN) that is
trained for vowel classification. The temporal evolution of those low-level
descriptors is modeled at the high-level within and across utterances via a
long short-term memory (LSTM) model that takes the final depression decision. A
modified version of the Local Interpretable Model-agnostic Explanations (LIME)
is further used to identify the impact of the low-level spectrotemporal vowel
variation on the decisions and observe the high-level temporal change of the
depression likelihood. The proposed method outperforms baselines that model the
spectrotemporal information in speech without integrating the vowel-based
information, as well as ML models trained with conventional prosodic and
spectrotemporal features. The conducted explainability analysis indicates that
spectrotemporal information corresponding to non-vowel segments less important
than the vowel-based information. Explainability of the high-level information
capturing the segment-by-segment decisions is further inspected for
participants with and without depression. The findings from this work can
provide the foundation toward knowledge-driven interpretable decision-support
systems that can assist clinicians to better understand fine-grain temporal
changes in speech data, ultimately augmenting mental health diagnosis and care.
