%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{VILLATORO-TELLO_Idiap-RR-09-2021,
                      author = {Villatoro-Tello, Esa{\'{u}} and Dubagunta, S. Pavankumar and Fritsch, Julian and Ram{\'{\i}}rez-de-la-Rosa, Gabriela and Motlicek, Petr and Magimai-Doss, Mathew},
                    keywords = {Alzheimer's disease, depression detection, Mental Lexicon, Multi-modal Approach, Raw Speech},
                    projects = {Idiap, TAPAS},
                       month = {7},
                       title = {Late Fusion of the Available Lexicon and Raw Waveform-based Acoustic Modeling for Depression and Dementia Recognition},
                        type = {Idiap-RR},
                      number = {Idiap-RR-09-2021},
                        year = {2021},
                 institution = {Idiap},
                        note = {Paper accepted for Publication in Interspeech 2021},
                    abstract = {Mental disorders, e.g. depression and dementia, are categorized as priority conditions according to the World Health Organization (WHO). When diagnosing,  psychologists employ structured questionnaires/interviews, and different cognitive tests. Although accurate, there is an increasing necessity of developing digital mental health support technologies to alleviate the burden faced by professionals. In this paper, we propose a multi-modal approach for modeling the communication process employed by patients being part of a clinical interview or a cognitive test. The language-based modality, inspired by the Lexical Availability (LA) theory from psycho-linguistics, identifies the most accessible vocabulary of the interviewed subject and use it as features in a classification process. The acoustic-based modality is processed by a Convolutional Neural Network (CNN) trained on signals of speech that predominantly contained voice source characteristics. At the end, a late fusion technique, based on majority voting, assigns the final classification. Results show the complementarity of both modalities, reaching an overall Macro-F1 of 84\% and 90\% for Depression and Alzheimer's dementia respectively.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2021/VILLATORO-TELLO_Idiap-RR-09-2021.pdf}
}