通过使用深度语言模型转移学习来表征语音流性
Joe Mole1,2, Amy Nelson2, Edgar Chan1,2
1Department of Neuropsychology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.
Brain communications
|December 4, 2023
概括
这项研究分析了前额外伤病患者的语音流性测试中的词汇选择. 定性分析和大型语言模型揭示了错误和单词选择的独特模式,改善了额头损伤检测.
科学领域:
- 神经科学是一个神经科学.
- 心理语言学 心理语言学
- 计算语言学 计算语言学
背景情况:
- 语音流利任务传统上侧重于响应量,忽视了定性词汇选择模式.
- 潜在的神经疾病可以影响流利性任务中的正确和错误的单词选择.
- 之前的分析没有全面检查与大脑损伤相关的语音流性表现的定性特征.
研究的目的:
- 进行第一个全面的正确和不正确的单词在语音 ("S") 流利性测试生成的第一个全面的定性分析.
- 调查大型语言模型和随机区块建模的实用性,以分析语音流的单词序列.
- 为了确定深度语言表示是否可以使用语音流性数据来改善额头病变的检测.
主要方法:
- 在语音流性任务中生成的单个单词的定性分析,分类错误,低频单词和集群/切换.
- 基于生成预训练变压器3 (GPT-3) 的词序的深度语言表示的随机块建模.
- 预测建模用于评估使用GPT-3衍生表示与原生特征检测额头病变的准确性.
主要成果:
- 定性分析揭示了病变特定的模式:不横向的前部效应用于亵,左侧前部效应用于名词/变换,左侧后部效应用于坚持,左侧前部效应用于低频正确单词.
- 大规模的语言模型分析确定了五个不同的社区,它们具有与人口统计和临床特征相关的独特词汇选择模式.
- 使用GPT-3表示的预测模型在预测额头病变方面表现出更高的准确性,而不是使用本地特征的模型.
结论:
- 在前额病变患者中,声学流性表现表现出特有的定性模式.
- 使用大型语言模型和随机区块建模来描述语音流性的定性特征,提供了重要的推断和诊断价值.
- 这种方法提高了流利性任务的潜力,用于检测和理解神经疾病的影响,如额头病变.
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