  Speech-based depression detection tools could help early screening of
depression. Here, we address two issues that may hinder the clinical
practicality of such tools: segment-level labelling noise and a lack of model
interpretability. We propose a speech-level Audio Spectrogram Transformer to
avoid segment-level labelling. We observe that the proposed model significantly
outperforms a segment-level model, providing evidence for the presence of
segment-level labelling noise in audio modality and the advantage of
longer-duration speech analysis for depression detection. We introduce a
frame-based attention interpretation method to extract acoustic features from
prediction-relevant waveform signals for interpretation by clinicians. Through
interpretation, we observe that the proposed model identifies reduced loudness
and F0 as relevant signals of depression, which aligns with the speech
characteristics of depressed patients documented in clinical studies.
