This paper introduces a novel speech-based depression score prediction paradigm, the 2-stage ranking prediction framework, and highlights the benefits it brings to depression prediction. Conventional regression approaches aim to discern a single functional relationship between speech features and depression scores, making an implicit assumption about the existence of a single fixed relationship between the features and scores. However, as the relationship between severity of depression and the clinical score may vary over the range of the assessment scale, this style of analysis may not be suited to depression prediction. The proposed framework on the other hand, imposes a series of partitions on the feature space, with each partition corresponding to a distinct predefined range of depression scores, and predicts the score based on measures of membership to each partition. This approach provides additional flexibility by allowing different rankings to be learnt for different depression scores, and relaxes assumptions made by conventional regression approaches. Results demonstrate the framework's suitability for depression score prediction: different 2-stage implementations, based on heterogeneous feature extraction and modelling approaches, produce state-of-the-art results on the AVEC-2013 dataset. It is also demonstrated that, unlike fusion of conventional regression systems, the fusion of two-stage systems consistently improves prediction performance.

中文翻译：

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用于从语音预测抑郁分数的广义两阶段秩回归框架

本文介绍了一种新的基于语音的抑郁评分预测范式，即 2 阶段排名预测框架，并重点介绍了它为抑郁预测带来的好处。传统的回归方法旨在辨别语音特征和抑郁分数之间的单一函数关系，隐含假设特征和分数之间存在单一的固定关系。然而，由于抑郁症的严重程度和临床评分之间的关​​系可能在评估量表的范围内有所不同，这种分析方式可能不适合抑郁症预测。另一方面，所提出的框架在特征空间上强加了一系列分区，每个分区对应于不同的预定义抑郁分数范围，并根据每个分区的成员资格来预测分数。这种方法通过允许为不同的抑郁分数学习不同的排名提供了额外的灵活性，并放宽了传统回归方法所做的假设。结果证明了该框架对抑郁评分预测的适用性：基于异构特征提取和建模方法的不同 2 阶段实现在 AVEC-2013 数据集上产生了最先进的结果。还表明，与传统回归系统的融合不同，两阶段系统的融合不断提高预测性能。并放宽了传统回归方法所做的假设。结果证明了该框架对抑郁评分预测的适用性：基于异构特征提取和建模方法的不同 2 阶段实现在 AVEC-2013 数据集上产生了最先进的结果。还表明，与传统回归系统的融合不同，两阶段系统的融合不断提高预测性能。并放宽了传统回归方法所做的假设。结果证明了该框架对抑郁评分预测的适用性：基于异构特征提取和建模方法的不同 2 阶段实现在 AVEC-2013 数据集上产生了最先进的结果。还表明，与传统回归系统的融合不同，两阶段系统的融合不断提高预测性能。