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:严重抑郁症(MDD)是全世界常见的心理健康问题,具有高的相关社会经济成本。因此,对MDD的预测和自动检测可以对社会产生巨大影响。作为一个非侵入性的、容易收集信号的演讲,是有助于诊断和评估MDD的一个很有希望的标志。在这方面,作为远程评估疾病和严重抑郁症重发(RADAR-MDD)研究方案的一部分,收集了语言样本。RADAR-MDD是一个观察组研究,从西班牙、联合王国和荷兰一批有MDD历史的人那里收集了演讲和其他数字生物标志。在本文中,RADAR-MDD语音资料作为实验框架,用于测试SDDD的测序和测序模型的功效。此外,还提出了一种新型培训方法,它基于我们选择更模糊的示范培训样本,并受到课程学习模式的启发。 RADAR-M语音资料库作为一个实验框架,用于测试一个从顺序到顺序测序的模型,我们每次测算结果的不断改进SARDDDR的学习过程。