Major Depressive Disorder (MDD) is a common worldwide mental health issue with high associated socioeconomic costs. The prediction and automatic detection of MDD can, therefore, make a huge impact on society. Speech, as a non-invasive, easy to collect signal, is a promising marker to aid the diagnosis and assessment of MDD. In this regard, speech samples were collected as part of the Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD) research programme. RADAR-MDD was an observational cohort study in which speech and other digital biomarkers were collected from a cohort of individuals with a history of MDD in Spain, United Kingdom and the Netherlands. In this paper, the RADAR-MDD speech corpus was taken as an experimental framework to test the efficacy of a Sequence-to-Sequence model with a local attention mechanism in a two-class depression severity classification paradigm. Additionally, a novel training method, HARD-Training, is proposed. It is a methodology based on the selection of more ambiguous samples for the model training, and inspired by the curriculum learning paradigm. HARD-Training was found to consistently improve - with an average increment of 8.6% - the performance of our classifiers for both of two speech elicitation tasks used and each collection site of the RADAR-MDD speech corpus. With this novel methodology, our Sequence-to-Sequence model was able to effectively detect MDD severity regardless of language. Finally, recognising the need for greater awareness of potential algorithmic bias, we conduct an additional analysis of our results separately for each gender.

翻译：重性抑郁障碍（MDD）是一种全球常见的心理健康问题，其相关社会经济成本高昂。因此，MDD的预测与自动检测能够对社会产生巨大影响。语音作为一种非侵入性、易于采集的信号，是辅助MDD诊断与评估的前景可观的生物标志物。为此，本研究从“重性抑郁障碍远程评估疾病与复发”（RADAR-MDD）研究项目中收集了语音样本。RADAR-MDD是一项观察性队列研究，对象包括西班牙、英国和荷兰有MDD病史的个体，从中采集了语音及其他数字生物标志物。本文以RADAR-MDD语音语料库为实验框架，检验了带有局部注意力机制的序列到序列（Sequence-to-Sequence）模型在二分类抑郁严重程度分类范式中的有效性。此外，本文提出了一种新型训练方法——HARD-Training。该方法受课程学习范式启发，基于选择更具歧义性的样本进行模型训练。研究发现，HARD-Training能够持续提升分类器性能（平均提升8.6%），这一提升在RADAR-MDD语音语料库的两种语音诱发任务及每个采集站点中均得到验证。借助这一新型方法，我们的序列到序列模型能够有效跨语言检测MDD严重程度。最后，鉴于对潜在算法偏差需提高认识，我们针对不同性别分别进行了额外结果分析。