ArticleJournal of biomedical informatics2022
Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS).
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Speech-based computational approaches for classification and symptom monitoring in schizophrenia spectrum disorders: a systematic review and meta-analysis.BMC psychiatry · 2026Pooled it
- Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.Neuropsychologia · 2026Article
- Perplexity and proximity: Large language model perplexity complements semantic distance metrics for the detection of incoherent speech.Journal of biomedical informatics · 2025Article
- Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.Translational psychiatry · 2025Article
- Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder.... Workshop on Computational Linguistics and Clinical Psychology · 2025Article
- Coherence and comprehensibility: Large language models predict lay understanding of health-related content.Journal of biomedical informatics · 2025Article
- Parent Psychopathology and Behavioral Effects on Child Brain-Symptom Networks in the ABCD Study.Journal of the American Academy of Child and Adolescent Psychiatry · 2024Article
- Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures.Translational psychiatry · 2024Article
- Useful blunders: Can automated speech recognition errors improve downstream dementia classification?Journal of biomedical informatics · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Formal thought disorder (ThD) is a clinical sign of schizophrenia amongst other serious mental health conditions. ThD can be recognized by observing incoherent speech - speech in which it is difficult to perceive connections between successive utterances and lacks a clear global theme. Automated assessment of the coherence of speech in patients with schizophrenia has been an active area of research for over a decade, in an effort to develop an objective and reliable instrument through which to quantify ThD. However, this work has largely been conducted in controlled settings using structured interviews and depended upon manual transcription services to render audio recordings amenable to computational analysis. In this paper, we present an evaluation of such automated methods in the context of a fully automated system using Automated Speech Recognition (ASR) in place of a manual transcription service, with "audio diaries" collected in naturalistic settings from participants experiencing Auditory Verbal Hallucinations (AVH). We show that performance lost due to ASR errors can often be restored through the application of Time-Series Augmented Representations for Detection of Incoherent Speech (TARDIS), a novel approach that involves treating the sequence of coherence scores from a transcript as a time-series, providing features for machine learning. With ASR, TARDIS improves average AUC across coherence metrics for detection of severe ThD by 0.09; average correlation with human-labeled derailment scores by 0.10; and average correlation between coherence estimates from manual and ASR-derived transcripts by 0.29. In addition, TARDIS improves the agreement between coherence estimates from manual transcripts and human judgment and correlation with self-reported estimates of AVH symptom severity. As such, TARDIS eliminates a fundamental barrier to the deployment of automated methods to detect linguistic indicators of ThD to monitor and improve clinical care in serious mental illness.
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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.