使用Oralstats对不同语篇类型的说话人进行韵律表征

IF 0.2 Q4 LINGUISTICS
Loquens Pub Date : 2022-06-28 DOI:10.3989/loquens.2021.079
Adrián Cabedo Nebot
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引用次数: 0

摘要

本文对4位西班牙政治家在4种不同话语类型(电视集会、新闻发布会、政治集会和采访)中的437个语调短语、2777个单词和12520个电话进行了韵律和形态句法分析。基于法律语言学、社会语言学、语音学和计算语言学的方法,开发了一套称为Oralstats的R脚本,它允许对与PRAAT一致的声学和文本数据进行多元、动态和交互式分析。通过这样做,提出了一种新的计算方法来寻找单个法医标记,结合语音和语言因素,如词汇或形态句法值。数据探索来自于通过决策树、热图和箱线图收集的韵律和形态句法变量的可视化。研究结果显示,在按类型区分自己时,说话者的行为方式非常相似;然而,在对抗性类型中,我们发现了一些意想不到的语音标记,例如低强度或低音调(预期的行为相反),从而使我们能够挑出一些说话者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Oralstats for prosodic characterisation of speakers in different discourse genres
This paper presents the prosodic and morphosyntactic analysis of 437 intonational phrases, 2777 words and 12520 phones registered from the speech of four Spanish politicians in four different discourse genres (TV gathering, press conference, political rally and interview). Based on methods from forensic linguistics, sociolinguistics, phonetics and computational linguistics, a set of R scripts called Oralstats has been developed, which allows to carry out a multivariate, dynamic and interactive analysis of acoustic and textual data aligned with PRAAT. By doing so, a new computational approach is suggested for finding individual forensic marks, combining both phonetic and linguistic factors, such as lexical or morphosyntactic values. Data exploration comes from the visualization of prosodic and morphosyntactic variables collected through decision trees, heatmaps and boxplots. The results show how speakers behave in a significantly similar way when it comes to differentiating themselves by genre; however, in confrontational genres some unexpected phonic marks were found, such as low intensity or low pitch, (the expected behavior being the opposite), thus enabling us to single out some of the speakers.
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来源期刊
Loquens
Loquens LINGUISTICS-
CiteScore
0.30
自引率
0.00%
发文量
6
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