Generalized Two-Stage Rank Regression Framework for Depression Score Prediction from Speech

Abstract

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.