Browsing by Author "Kaya, Heysem"

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Citation - WoS: 30
Citation - Scopus: 40
Predicting Depression and Emotions in the Cross-Roads of Cultures, Para-Linguistics, and Non-Linguistics
(Assoc Computing Machinery, 2019) Kaya, Heysem; Fedotov, Dmitrii; Dresvyanskiy, Denis; Doyran, Metehan; Mamontov, Danila; Markitantov, Maxim; Salah, Albert Ali
Cross-language, cross-cultural emotion recognition and accurate prediction of affective disorders are two of the major challenges in affective computing today. In this work, we compare several systems for Detecting Depression with AI Sub-challenge (DDS) and Cross-cultural Emotion Sub-challenge (CES) that are published as part of the Audio-Visual Emotion Challenge (AVEC) 2019. For both sub-challenges, we benefit from the baselines, while introducing our own features and regression models. For the DDS challenge, where ASR transcripts are provided by the organizers, we propose simple linguistic and word-duration features. These ASR transcript-based features are shown to outperform the state of the art audio visual features for this task, reaching a test set Concordance Correlation Coefficient (CCC) performance of 0.344 in comparison to a challenge baseline of 0.120. Our results show that non-verbal parts of the signal are important for detection of depression, and combining this with linguistic information produces the best results. For CES, the proposed systems using unsupervised feature adaptation outperform the challenge baselines on emotional primitives, reaching test set CCC performances of 0.466 and 0.499 for arousal and valence, respectively.
Citation - WoS: 3
Citation - Scopus: 4
The sound of silence: Breathing analysis for finding traces of trauma and depression in oral history archives
(Oxford Academic, 2021) Akdag Salah, Almila; Salah, Albert Ali; Kaya, Heysem; Doyran, Metehan; Kavcar, Evrim
Many people experience a traumatic event during their lifetime. In some extraordinary situations, such as natural disasters, war, massacres, terrorism, or mass migration, the traumatic event is shared by a community and the effects go beyond those directly affected. Today, thanks to recorded interviews and testimonials, many archives and collections exist that are open to researchers of trauma studies, holocaust studies, and historians, among others. These archives act as vital testimonials for oral history, politics, and human rights. As such, they are usually either transcribed or meticulously indexed. In this work, we propose to look at the nonverbal signals emitted by victims of various traumatic events when they describe the trauma and we seek to render these for novel representations without taking into account the explicit verbal content. Our preliminary paralinguistic analysis on a manually annotated collection of testimonials from different archives, as well as on a corpus prepared for depression and post-traumatic stress disorder detection indicates a tentative connection between breathing and emotional states of speakers, which opens up new possibilities of exploring oral history archives.