Depression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.

中文翻译：

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使用声学和文本特征从多语言会话语音数据中评估抑郁程度

抑郁症是世界范围内普遍存在的心理健康问题，给经济带来了重大负担。其早期诊断和治疗对于降低成本甚至挽救生命至关重要。实现这一目标的一个关键方面是使用技术并使用自动化代理以相对便宜的方式远程监控抑郁症。使用视听特征以及对话语音转录的文本分析来自动评估抑郁程度已经有很多努力。然而，数据收集的困难和可用于研究的数据量有限带来了阻碍算法成功的挑战。本文的两个新贡献之一是利用来自多种语言的数据库进行声学特征选择。由于可以从语音中提取大量特征，鉴于可用的训练数据很少，有效的数据选择对于成功至关重要。我们提出的多语言方法在选择比基线算法更好的特征方面是有效的，这显着提高了抑郁症评估的准确性。该论文的第二个贡献是提取用于抑郁评估的基于文本的特征，并使用一种新颖的算法来融合基于文本和语音的分类器，从而进一步提高了性能。