ArticleNPP - digital psychiatry and neuroscience2025
Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks.
Article in NPP - digital psychiatry and neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech-based computational approaches for classification and symptom monitoring in schizophrenia spectrum disorders: a systematic review and meta-analysis.BMC psychiatry · 2026Pooled it
- Integrated multiobjective optimization of RFSSW parameters for AA2024-T3 using ANOVA machine learning and NSGA II.Scientific reports · 2025Article
- Quantitative semiology: harnessing AI-generated teaching signals in psychiatry.Journal of psychiatry & neuroscience : JPN · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Speech-based indices are promising objective biomarkers for identifying schizophrenia and monitoring symptom burden. Static acoustic features show potential but often overlook time-varying acoustic cues that clinicians naturally evaluate-such as negative symptoms-during clinical interviews. A similar dynamic, unfiltered approach can be applied using speech spectrograms, preserving acoustic-temporal nuances. Here, we investigate if this method has the potential to assist in the determination of diagnostic and symptom severity status. Speech recordings from 319 participants (227 with schizophrenia spectrum disorders, 92 healthy controls) were segmented into 10 s fragments of uninterrupted audio (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.