Abstract：
This study aims to further our understanding of prosodic entrainment and its different subtypes by analyzing a single corpus of conversations with 12 different methods and comparing the subsequent results.
Entrainment on three fundamental frequency features was analyzed in a subset of recordings from the LUCID corpus (Baker & Hazan, 2011) using the following methods: global proximity, global convergence, local proximity, local convergence, local synchrony (Levitan & Hirschberg, 2011), prediction using linear mixed-effects models (Schweitzer & Lewandowski, 2013), geometric approach (Lehnert-LeHouillier, Terrazas, & Sandoval, 2020), time-aligned moving average (Kousidis et al., 2008), HYBRID method (De Looze et al., 2014), cross-recurrence quantification analysis (e.g., Fusaroli & Tylén, 2016), and windowed, lagged cross-correlation (Boker et al., 2002). We employed entrainment measures on a local timescale (i.e., on adjacent utterances), a global timescale (i.e., over larger time frames), and a time series-based timescale that is larger than adjacent utterances but smaller than entire conversations.
We observed variance in results of different methods.
Results suggest that each method may measure a slightly different type of entrainment. The complex implications this has for existing and future research are discussed.
Entrainment on three fundamental frequency features was analyzed in a subset of recordings from the LUCID corpus (Baker & Hazan, 2011) using the following methods: global proximity, global convergence, local proximity, local convergence, local synchrony (Levitan & Hirschberg, 2011), prediction using linear mixed-effects models (Schweitzer & Lewandowski, 2013), geometric approach (Lehnert-LeHouillier, Terrazas, & Sandoval, 2020), time-aligned moving average (Kousidis et al., 2008), HYBRID method (De Looze et al., 2014), cross-recurrence quantification analysis (e.g., Fusaroli & Tylén, 2016), and windowed, lagged cross-correlation (Boker et al., 2002). We employed entrainment measures on a local timescale (i.e., on adjacent utterances), a global timescale (i.e., over larger time frames), and a time series-based timescale that is larger than adjacent utterances but smaller than entire conversations.
We observed variance in results of different methods.
Results suggest that each method may measure a slightly different type of entrainment. The complex implications this has for existing and future research are discussed.
摘要：
目的：本研究旨在通过使用12种不同方法分析单个语料库的对话并比较后续结果，进一步了解我们对韵律夹带及其不同亚型的理解。
方法：在LUCID语料库的一部分录音中分析了三个基本频率特征的夹带（Baker＆Hazan，2011)使用以下方法：全球邻近度，全球融合，本地接近度,局部收敛,局部同步(Levitan&Hirschberg，2011),使用线性混合效应模型进行预测(Schweitzer&Lewandowski，2013),几何方法(Lehnert-LeHouillier，Terrazas,&Sandoval,2020),时间对齐移动平均线(Kousidis等人。,2008),混合方法(DeLooze等人,2014),交叉复发定量分析(例如，Fusaroli&Tylén,2016),和窗口，滞后互相关(Boker等人。,2002).我们在当地时间尺度上采用了夹带措施(即，在相邻的话语上)，全局时间尺度(即，在更大的时间范围内)，以及基于时间序列的时间尺度，该时间尺度大于相邻的话语，但小于整个对话。
结果：我们观察到不同方法结果的差异。
结论：结果表明，每种方法测量的夹带类型可能略有不同。讨论了这对现有和未来研究的复杂含义。
方法：在LUCID语料库的一部分录音中分析了三个基本频率特征的夹带（Baker＆Hazan，2011)使用以下方法：全球邻近度，全球融合，本地接近度,局部收敛,局部同步(Levitan&Hirschberg，2011),使用线性混合效应模型进行预测(Schweitzer&Lewandowski，2013),几何方法(Lehnert-LeHouillier，Terrazas,&Sandoval,2020),时间对齐移动平均线(Kousidis等人。,2008),混合方法(DeLooze等人,2014),交叉复发定量分析(例如，Fusaroli&Tylén,2016),和窗口，滞后互相关(Boker等人。,2002).我们在当地时间尺度上采用了夹带措施(即，在相邻的话语上)，全局时间尺度(即，在更大的时间范围内)，以及基于时间序列的时间尺度，该时间尺度大于相邻的话语，但小于整个对话。
结果：我们观察到不同方法结果的差异。
结论：结果表明，每种方法测量的夹带类型可能略有不同。讨论了这对现有和未来研究的复杂含义。
