A Multimodal Framework for the Assessment of the Schizophrenia Spectrum

Autor:	Premananth, Gowtham, Siriwardena, Yashish M., Resnik, Philip, Bansal, Sonia, Kelly, Deanna L., Espy-Wilson, Carol
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
DOI:	10.21437/Interspeech.2024-2224
Popis:	This paper presents a novel multimodal framework to distinguish between different symptom classes of subjects in the schizophrenia spectrum and healthy controls using audio, video, and text modalities. We implemented Convolution Neural Network and Long Short Term Memory based unimodal models and experimented on various multimodal fusion approaches to come up with the proposed framework. We utilized a minimal Gated multimodal unit (mGMU) to obtain a bi-modal intermediate fusion of the features extracted from the input modalities before finally fusing the outputs of the bimodal fusions to perform subject-wise classifications. The use of mGMU units in the multimodal framework improved the performance in both weighted f1-score and weighted AUC-ROC scores. Comment: Accepted to be presented at Interspeech 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.09706 Zobrazit plný text záznamu View this record from Arxiv