用机器学习自动识别失语症患者的语言信息单元，提升临床评估效率。
Practical Machine Learning for Aphasic Discourse Analysis
- 采用五种监督学习模型，基于人工标注的失语症说话人图片描述数据训练。
- 识别词语正确性准确率达99.5%，但识别有效信息单元准确率仅82.4%。
- 为临床语言病理学家提供自动化分析工具，适合语言康复研究者参考。
分析口语话语是量化失语症患者语言能力的有效方法。其中一种常见方式是评估话语的信息量，即在总词汇数中，有多少是上下文相关且准确的。这种分析称为正确信息单元（CIU）分析，是言语语言病理学家（SLPs）最常用的对话分析之一。然而，由于需要语言病理学家手动编码和分析语音数据，该方法在临床应用中仍受限。近年来，机器学习（ML）的进步旨在通过自动化建模话语的命题、宏观结构、语用及多模态维度来减轻人力负担。本研究评估了五种机器学习模型在图片描述任务中可靠识别正确信息单元（CIUs）的表现。五个监督学习模型使用随机选取的人工标注转录本及对应词语与CIU进行训练。基线模型在词与非词的区分上表现优异，所有模型准确率均接近完美（0.995），AUC范围为0.914至0.995。相比之下，CIU与非CIU的区分表现波动较大，其中最近邻（k-NN）模型准确率最高（0.824），其次为最高AUC（0.787）。结果表明，尽管模型能有效区分词与非词，但准确识别CIU仍具挑战性。
原文摘要 · Abstract (English)
Analyzing spoken discourse is a valid means of quantifying language ability in persons with aphasia. There are many ways to quantify discourse, one common way being to evaluate the informativeness of the discourse. That is, given the total number of words produced, how many of those are context-relevant and accurate. This type of analysis is called Correct Information Unit (CIU) analysis and is one of the most prevalent discourse analyses used by speech-language pathologists (SLPs). Despite this, CIU analysis in the clinic remains limited due to the manual labor needed by SLPs to code and analyze collected speech. Recent advances in machine learning (ML) seek to augment such labor by automating modeling of propositional, macrostructural, pragmatic, and multimodal dimensions of discourse. To that end, this study evaluated five ML models for reliable identification of Correct Information Units (CIUs, Nicholas & Brookshire, 1993), during a picture description task. The five supervised ML models were trained using randomly selected human-coded transcripts and accompanying words and CIUs from persons with aphasia. The baseline model training produced a high accuracy across transcripts for word vs non-word, with all models achieving near perfect performance (0.995) with high AUC range (0.914 min, 0.995 max). In contrast, CIU vs non-CIU showed a greater variability, with the k-nearest neighbor (k-NN) model the highest accuracy (0.824) and second highest AUC (0.787). These findings indicate that while the supervised ML models can distinguish word from not word, identifying CIUs is challenging.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。