Speech-based assessment of the schizophrenia spectrum has been widely researched over in the recent past. In this study, we develop a deep learning framework to estimate schizophrenia severity scores from speech using a feature fusion approach that fuses articulatory features with different self-supervised speech features extracted from pre-trained audio models. We also propose an auto-encoder-based self-supervised representation learning framework to extract compact articulatory embeddings from speech. Our top-performing speech-based fusion model with Multi-Head Attention (MHA) reduces Mean Absolute Error (MAE) by 9.18% and Root Mean Squared Error (RMSE) by 9.36% for schizophrenia severity estimation when compared with the previous models that combined speech and video inputs.
@article{arxiv.2411.06033,
title = {Speech-Based Estimation of Schizophrenia Severity Using Feature Fusion},
author = {Gowtham Premananth and Carol Espy-Wilson},
journal= {arXiv preprint arXiv:2411.06033},
year = {2024}
}