Individuals that have incurred trauma due to a suicide attempt often acquire residual health complications, such as cognitive, mood, and speech-language disorders. Due to limited access to suicidal speech audio corpora, behavioral differences in patients with a history of suicidal ideation and/or behavior have not been thoroughly examined using subjective voice quality and manual disfluency measures. In this study, we examine the Butler-Brown Read Speech (BBRS) database that includes 20 healthy controls with no history of suicidal ideation or behavior (HC group) and 226 psychiatric inpatients with recent suicidal ideation (SI group) or a recent suicide attempt (SA group). During read aloud sentence tasks, SI and SA groups reveal poorer average subjective voice quality composite ratings when compared with individuals in the HC group. In particular, the SI and SA groups exhibit average ‘grade’ and ‘roughness’ voice quality scores four to six times higher than those of the HC group. We demonstrate that manually annotated voice quality measures, converted into a low-dimensional feature vector, help to identify individuals with recent suicidal ideation and behavior from a healthy population, generating an automatic classification accuracy of up to 73%. Furthermore, our novel investigation of manual speech disfluencies (e.g., manually detected hesitations, word/phrase repeats, malapropisms, speech errors, non-self-correction) shows that inpatients in the SI and SA groups produce on average approximately twice as many hesitations and four times as many speech errors when compared with individuals in the HC group. We demonstrate automatic classification of inpatients with a suicide history from individuals with no suicide history with up to 80% accuracy using manually annotated speech disfluency features. Knowledge regarding voice quality and speech disfluency behaviors in individuals with a suicide history presented herein will lead to a better understanding of this complex phenomenon and thus contribute to the future development of new automatic speech-based suicide-risk identification systems.

因企图自杀而遭受创伤的个体通常会出现残留的健康并发症，例如认知、情绪和语言障碍。由于对自杀语音语料库的访问有限，有自杀意念和/或行为史的患者的行为差异尚未使用主观语音质量和手动不流畅测量进行彻底检查。在这项研究中，我们检查了 Butler-Brown Read Speech (BBRS) 数据库，该数据库包括 20 名没有自杀意念或行为史的健康对照（HC 组）和 226 名近期有自杀意念或最近有自杀企图的精神病住院患者（SA组）。在朗读句子任务中，与 HC 组中的个人相比，SI 和 SA 组显示出较差的平均主观语音质量综合评分。特别是，SI 和 SA 组的平均“等级”和“粗糙度”语音质量得分比 HC 组高四到六倍。我们证明了手动注释的语音质量度量，转换为低维特征向量，有助于从健康人群中识别最近有自杀意念和行为的个人，生成高达 73% 的自动分类准确率。此外，我们对人工言语不流畅（例如，人工检测到的犹豫、单词/短语重复、语言不当、言语错误、非自我纠正）的新调查表明，SI 和 SA 组的住院患者产生的犹豫和与 HC 组中的个体相比，言语错误是其四倍。我们展示了使用手动注释的语音不流畅特征自动分类有自杀史的住院患者和没有自杀史的个体，准确率高达 80%。此处介绍的有关有自杀史的个体的语音质量和言语不流畅行为的知识将有助于更好地理解这一复杂现象，从而有助于未来开发新的基于语音的自杀风险自动识别系统。