发表机构
Jožef Stefan Institute; Institute of Contemporary History; Faculty of Computer and Information Science, University of Ljubljana（约热夫·斯泰凡研究所; 当代历史研究所; 卢布尔雅那大学计算机与信息科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于ParlaSpeech 3.0语料库，对四个斯拉夫议会的政治家发言开展三项大规模研究，揭示情感对声学实现的影响、填充停顿的跨语言预测因子及主重音的词类差异，并提出相关研究议程。
AI 中文摘要
我们基于ParlaSpeech 3.0语料库中超过6000小时的语料，针对四种斯拉夫语言（克罗地亚语、捷克语、波兰语、塞尔维亚语）的议会口语开展三项大规模研究。第一项研究考察语句层面的情感如何影响声学实现：在四个议会中，消极发言始终表现为更高的音高、更大的强度和更快的语速，而在最积极的极端情况则会出现由唤醒驱动的次生上扬。第二项研究采用负二项式GEE模型对填充停顿频率进行建模，发现语速、年龄和情感是稳健的跨语言预测因子，而性别效应在南斯拉夫议会（男性产生的填充停顿更少）与西斯拉夫议会（无性别差异）之间存在反转——这一模式是单语言设计无法察觉的。第三项研究调查克罗地亚语中的主重音变化，发现说话者对早重音与晚重音的偏好在动词、形容词和名词中具有一致性，但在副词和专有名词中则相互分离。我们最后提出了涵盖语料库语音学、不流畅建模和政治修辞的研究议程。
英文摘要
We present three large-scale studies of spoken parliamentary speech across four Slavic languages (Croatian, Czech, Polish, Serbian), drawing on over 6,000 hours from the ParlaSpeech 3.0 corpus. The first study examines how utterance-level sentiment shapes acoustic realisation: negative speech is consistently produced with higher pitch, greater intensity, and faster rate across all four parliaments, with a secondary arousal-driven upturn at the most positive extreme. The second study models filled pause frequency using negative binomial GEE, finding that speech rate, age, and sentiment are robust cross-lingual predictors, while gender effects reverse between South Slavic (men produce fewer filled pauses) and West Slavic parliaments (no gender difference) - a pattern invisible to single-language designs. The third study investigates primary stress variation in Croatian, showing that speaker-level preferences for early versus late stress cohere across verbs, adjectives, and nouns but decouple for adverbs and proper nouns. We conclude with a research agenda spanning corpus phonetics, disfluency modelling, and political rhetoric.
Comments11 pages, 3 figures
