Multi-lingual depression-level assessment from conversational speech using acoustic and text features

Abstract

Depression is a common mental health problem around the world with a large burden on economies, well-being, hence productivity, of individuals. 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 voice technologies and monitor depression remotely and relatively inexpensively using automated agents. Although there has been efforts to automatically assess depression levels from audiovisual features, use of transcriptions along with the acoustic features has emerged as a more recent research venue. Moreover, difficulty in data collection and the limited amounts of data available for research are also challenges that are hampering the success of the algorithms. One of the novel contributions in this paper is to exploit the databases from multiple languages for feature selection. Since a large number of features can be extracted from speech and 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 and significantly improved the depression assessment accuracy. We also use text-based features for assessment and propose a novel strategy to fuse the text- and speech-based classifiers which further boosted the performance.