Speech-Based Prioritization for Schizophrenia Intervention
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909887468929024 |
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| author | Premananth, Gowtham Resnik, Philip Bansal, Sonia Kelly, Deanna L. Espy-Wilson, Carol |
| author_facet | Premananth, Gowtham Resnik, Philip Bansal, Sonia Kelly, Deanna L. Espy-Wilson, Carol |
| contents | Millions of people suffer from mental health conditions, yet many remain undiagnosed or receive delayed care due to limited clinical resources and labor-intensive assessment methods. While most machine-assisted approaches focus on diagnostic classification, estimating symptom severity is essential for prioritizing care, particularly in resource-constrained settings. Speech-based AI provides a scalable alternative by enabling automated, continuous, and remote monitoring, reducing reliance on subjective self-reports and time-consuming evaluations. In this paper, we introduce a speech-based model for pairwise comparison of schizophrenia symptom severity, leveraging articulatory and acoustic features. These comparisons are used to generate severity rankings via the Bradley-Terry model. Our approach outperforms previous regression-based models on ranking-based metrics, offering a more effective solution for clinical triage and prioritization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_03086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Speech-Based Prioritization for Schizophrenia Intervention Premananth, Gowtham Resnik, Philip Bansal, Sonia Kelly, Deanna L. Espy-Wilson, Carol Audio and Speech Processing Signal Processing Millions of people suffer from mental health conditions, yet many remain undiagnosed or receive delayed care due to limited clinical resources and labor-intensive assessment methods. While most machine-assisted approaches focus on diagnostic classification, estimating symptom severity is essential for prioritizing care, particularly in resource-constrained settings. Speech-based AI provides a scalable alternative by enabling automated, continuous, and remote monitoring, reducing reliance on subjective self-reports and time-consuming evaluations. In this paper, we introduce a speech-based model for pairwise comparison of schizophrenia symptom severity, leveraging articulatory and acoustic features. These comparisons are used to generate severity rankings via the Bradley-Terry model. Our approach outperforms previous regression-based models on ranking-based metrics, offering a more effective solution for clinical triage and prioritization. |
| title | Speech-Based Prioritization for Schizophrenia Intervention |
| topic | Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2511.03086 |