ISCA Archive Interspeech 2020
ISCA Archive Interspeech 2020

Affective Conditioning on Hierarchical Attention Networks Applied to Depression Detection from Transcribed Clinical Interviews

Danai Xezonaki, Georgios Paraskevopoulos, Alexandros Potamianos, Shrikanth Narayanan

In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject’s mood but also the use of language. To this end we use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica. Our analysis shows that individuals diagnosed with depression use affective language to a greater extent than not-depressed. Our experiments show that external affective information improves the performance of the proposed architecture in the General Psychotherapy Corpus and the DAIC-WoZ 2017 depression datasets, achieving state-of-the-art 71.6 and 70.3 using the test set, F1-scores respectively.