%Aigaion2 BibTeX export from Idiap Publications
%Monday 16 December 2024 07:46:56 PM

@INPROCEEDINGS{VILLATORO-TELLO_INTERSPEECH2021_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},
       projects = {Idiap, TAPAS},
          month = aug,
          title = {Late Fusion of the Available Lexicon and Raw Waveform-based Acoustic Modeling for Depression and Dementia Recognition},
      booktitle = {Proceedings of Interspeech 2021},
           year = {2021},
      publisher = {ISCA-International Speech Communication Association 2021},
       crossref = {VILLATORO-TELLO_Idiap-RR-09-2021},
       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. In 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/papers/2021/VILLATORO-TELLO_INTERSPEECH2021_2021.pdf}
}



crossreferenced publications: 
@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}
}