Browsing by Author "Besirli, A."
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ArticlePublication Open Access Depression-level assessment from multi-lingual conversational speech data using acoustic and text features(Springer Nature, 2020-11-17) Demiroğlu, Cenk; Besirli, A.; Özkanca, Yasin Sedar; Celik, S.; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Özkanca, Yasin SedarDepression 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.Conference paperPublication Open Access Multi-lingual depression-level assessment from conversational speech using acoustic and text features(International Speech Communication Association, 2018) Özkanca, Yasin Serdar; Demiroğlu, Cenk; Besirli, A.; Çelik, S.; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Özkanca, Yasin SerdarDepression 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.