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.

翻译：与抑郁相关的精神迟缓与发音制作方面的明显差异有关。本文调查了一种知识驱动的机器学习(ML)方法,该方法结合了在发音层的语调时光信息以辨别抑郁症。低层次的言语描述器由接受过发音分类培训的神经神经网络(CNN)学习。这些低层次描述器的时间演化模式建模于通过长期短期记忆模型(LSTM)在高层次的发音中和跨层次的发音中进行,该模型将作出最后的抑郁症决定。对本地解释模型解释(MLM)方法的修改版本被进一步用于确定低层次的语调时情调解释器对决定的影响,并观察抑郁症可能性的高层次时间变化。拟议方法优于基准,该基准建模在语音中的光谱信息中不包含基于誓言的信息,以及经过常规的语音和时光学特征培训的ML模型。进行的解释性分析后,可理解性解释性解释性解释性分析显示低层次的临床分析部分结论,最终可理解性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论性结论