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Speech-Based Estimation of Schizophrenia Severity Using Feature Fusion

2024-11-09 · Gowtham Premananth, Carol Espy-Wilson

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.

📄 PDF Abstract BibTeX arXiv:2411.06033

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Representation Learning

Methods 이 논문이 사용한 방법론

Attention 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Multi-Head Attention 설명 없음

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