Depression is a common mental disorder. Automatic depression detection tools
using speech, enabled by machine learning, help early screening of depression.
This paper addresses two limitations that may hinder the clinical
implementations of such tools: noise resulting from segment-level labelling and
a lack of model interpretability. We propose a bi-modal speech-level
transformer to avoid segment-level labelling and introduce a hierarchical
interpretation approach to provide both speech-level and sentence-level
interpretations, based on gradient-weighted attention maps derived from all
attention layers to track interactions between input features. We show that the
proposed model outperforms a model that learns at a segment level ($p$=0.854,
$r$=0.947, $F1$=0.947 compared to $p$=0.732, $r$=0.808, $F1$=0.768). For model
interpretation, using one true positive sample, we show which sentences within
a given speech are most relevant to depression detection; and which text tokens
and Mel-spectrogram regions within these sentences are most relevant to
depression detection. These interpretations allow clinicians to verify the
validity of predictions made by depression detection tools, promoting their
clinical implementations.

提出了一种使用双模式语音级转换器的自动抑郁症检测工具，以避免分段级别标注的噪声并引入分层解释方法，从而在语音级别和句子级别提供模型解释，其性能优于学习在分段级别的模型，并提供了有关抑郁症检测的相关句子、文本标记和 Mel 频谱图区域的解释，以验证抑郁症检测工具的预测有效性，推动其临床实施。