6 papers
Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Language
Tomek Rutowski, Amir Harati, Elizabeth Shriberg +3
Mental health risk prediction is a growing field in the speech community, but many studies are based on small corpora. This study illustrates how variations in test and train set s…
Optimizing Speech-Input Length for Speaker-Independent Depression Classification
Tomasz Rutowski, Amir Harati, Yang Lu +1
Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression classification, little is understo…
Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning
Tomasz Rutowski, Elizabeth Shriberg, Amir Harati +3
Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models for detecting depression, anxiety…
Robust Speech and Natural Language Processing Models for Depression Screening
Y. Lu, A. Harati, T. Rutowski +3
Depression is a global health concern with a critical need for increased patient screening. Speech technology offers advantages for remote screening but must perform robustly acros…
Cross-Demographic Portability of Deep NLP-Based Depression Models
Tomek Rutowski, Elizabeth Shriberg, Amir Harati +3
Deep learning models are rapidly gaining interest for real-world applications in behavioral health. An important gap in current literature is how well such models generalize over d…
Speech-Based Depression Prediction Using Encoder-Weight-Only Transfer Learning and a Large Corpus
Amir Harati, Elizabeth Shriberg, Tomasz Rutowski +3
Speech-based algorithms have gained interest for the management of behavioral health conditions such as depression. We explore a speech-based transfer learning approach that uses a…