ISCA Archive IberSPEECH 2022
ISCA Archive IberSPEECH 2022

Speech and Text Processing for Major Depressive Disorder Detection

Edward L. Campbell, Laura Docío Fernández, Nicholas Cummins, Carmen García Mateo

Major Depressive Disorder (MDD) is a common mental health issue these days. Its early diagnostic is vital to avoid bigger consequences and provide an appropriate treatment. Speech and utterance’s transcription of patients’ interviews contain useful information sources for the automatic screening of MDD. In this sense, speech- and text-based systems are proposed in this paper, using the DAIC-WOZ dataset as experimental framework. The speech-based one is a Sequence-to-Sequence (S2S) model with a local attention mechanism. The text-based one is based on GloVe features and a Convolutional Neural Network as classifier. A description of some of the more relevant results achieved by other research publications on DAIC-WOZ are described as well. The goal is to provide a better understanding of the context of our systems results. In general, the S2S architecture provides mostly better results than previous speechbased systems. The GloVe-CNN system shows even a better performance, leading to the idea that text is a more suitable information source for the detection of MDD when it is manually developed. However, to automatically obtain high quality transcriptions is not a straightforward task, which makes necessary the development of effective speech-based systems as the presented in this research work.

doi: 10.21437/IberSPEECH.2022-18

Cite as: Campbell, E.L., Fernández, L.D., Cummins, N., Mateo, C.G. (2022) Speech and Text Processing for Major Depressive Disorder Detection . Proc. IberSPEECH 2022, 86-90, doi: 10.21437/IberSPEECH.2022-18

  author={Edward L. Campbell and Laura Docío Fernández and Nicholas Cummins and Carmen García Mateo},
  title={{Speech and Text Processing for Major Depressive Disorder Detection }},
  booktitle={Proc. IberSPEECH 2022},