# Quantifying Articulatory Coordination as a Biomarker for Schizophrenia

arXiv: https://arxiv.org/abs/2511.03084 · HTML: https://mlsanity.com/paper/2511.03084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables](https://mlsanity.com/paper/2309.09220/) (2023)
- [A Multimodal Framework for the Assessment of the Schizophrenia Spectrum](https://mlsanity.com/paper/2406.09706/) (2024)
- [A multi-modal approach for identifying schizophrenia using cross-modal attention](https://mlsanity.com/paper/2309.15136/) (2023)
- [Speech-Based Estimation of Schizophrenia Severity Using Feature Fusion](https://mlsanity.com/paper/2411.06033/) (2024)
- [Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation](https://mlsanity.com/paper/2505.16044/) (2025)

## Built on by (most cited first, 1 of 1)

- [Articulatory Entrainment and Coordination Complexity in Spontaneous Autistic and Non-autistic Dialogue](https://mlsanity.com/paper/2610.04071/) (2026)

## Abstract (arXiv)

Advances in artificial intelligence (AI) and deep learning have improved diagnostic capabilities in healthcare, yet limited interpretability continues to hinder clinical adoption. Schizophrenia, a complex disorder with diverse symptoms including disorganized speech and social withdrawal, demands tools that capture symptom severity and provide clinically meaningful insights beyond binary diagnosis. Here, we present an interpretable framework that leverages articulatory speech features through eigenspectra difference plots and a weighted sum with exponential decay (WSED) to quantify vocal tract coordination. Eigenspectra plots effectively distinguished complex from simpler coordination patterns, and WSED scores reliably separated these groups, with ambiguity confined to a narrow range near zero. Importantly, WSED scores correlated not only with overall BPRS severity but also with the balance between positive and negative symptoms, reflecting more complex coordination in subjects with pronounced positive symptoms and the opposite trend for stronger negative symptoms. This approach offers a transparent, severity-sensitive biomarker for schizophrenia, advancing the potential for clinically interpretable speech-based assessment tools.