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<p>Computational analysis of speech enables precise, objective measurements of a behavioral signal closely linked to manic episodes. In a prospective cohort study conducted in Rochester, MN <b>(</b>ClinicalTrials.gov Identifier: NCT05956340<b>)</b>, eighteen English-speaking adults voluntarily hospitalized for a manic episode of Bipolar I Disorder between August 2023 and September 2024 were enrolled. Participants completed semi-structured research interviews during both acute episodes and remission, with manic symptom severity assessed at each session using the Young Mania Rating Scale (YMRS). Across 57 recorded interviews, temporal prosodic features were extracted using a novel end-to-end computational pipeline. After adjusting for individual differences and moderating effects, participant articulation rate (β = 0.24, <i>p</i> = 0.0038), speech rate variability (β = 0.23, <i>p</i> = 0.0041), floor transfer offset (β = −0.31, <i>p</i> = 0.007), and speaking duration (β = 0.54, <i>p</i> &lt; 0.001) were significantly associated with manic symptom severity. These exploratory findings were largely independent of medication effects, as well as the presence of psychotic or mixed symptoms. These temporal elements classified manic (CV AUC = 0.85) and remission (CV AUC = 0.83) status, highlighting their potential as monitoring biomarkers for manic episodes.</p>

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Temporal elements of speech in mania

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Jeremiah B. Joyce,
  • Ivan N. Ayala,
  • Sanjeev Mishra,
  • George Chatzisofroniou,
  • Erik Clemens,
  • Hang Yu,
  • Tyler Kendall,
  • Zachi Attia,
  • Baihan Lin,
  • Mark A. Frye

摘要

Computational analysis of speech enables precise, objective measurements of a behavioral signal closely linked to manic episodes. In a prospective cohort study conducted in Rochester, MN (ClinicalTrials.gov Identifier: NCT05956340), eighteen English-speaking adults voluntarily hospitalized for a manic episode of Bipolar I Disorder between August 2023 and September 2024 were enrolled. Participants completed semi-structured research interviews during both acute episodes and remission, with manic symptom severity assessed at each session using the Young Mania Rating Scale (YMRS). Across 57 recorded interviews, temporal prosodic features were extracted using a novel end-to-end computational pipeline. After adjusting for individual differences and moderating effects, participant articulation rate (β = 0.24, p = 0.0038), speech rate variability (β = 0.23, p = 0.0041), floor transfer offset (β = −0.31, p = 0.007), and speaking duration (β = 0.54, p < 0.001) were significantly associated with manic symptom severity. These exploratory findings were largely independent of medication effects, as well as the presence of psychotic or mixed symptoms. These temporal elements classified manic (CV AUC = 0.85) and remission (CV AUC = 0.83) status, highlighting their potential as monitoring biomarkers for manic episodes.