Umm With Transformers? Insights from Filled Pause Use across Four Slavic Parliaments

Jul 16, 2026

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Filled pauses (FPs) are a universal feature of spontaneous speech, yet most research depends on small, single-language datasets that limit generalisation. The study analyses about 4,000 hours of parliamentary speech across four related Slavic languages: Croatian, Czech, Polish, and Serbian. Transformer-based automatic detection estimates FP occurrences, while a Mundlak-corrected GEE model separates within- from between-speaker effects. Age and speech rate show negative associations; gender effects differ by language. Sentiment, orientation, and power status show a consistent positive sentiment–FP link, with opposition speakers tending to use fewer FPs than governing coalitions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

Abstract

Introduction

Related Work

Automatic FP Identification

Gender

Age

Umm... With Transformers? Insights from Filled Pause Use across Four Slavic Parliaments Ivan Porupski 1 ,∗∗, Branimir Dropuljic´ 2 ,3, Nikola Ljubeˇsic´ 1 ,4 ,5 1 Department of Knowledge Technologies, Joˇzef Stefan Institute, Ljubljana, Slovenia 2 TransUnion, Zagreb, Croatia 3 Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia 4 Faculty of Computer and Information Science, University of Ljubljana, Slovenia 5 Institute of Contemporary History, Ljubljana, Slovenia [ivan.porupski@ijs.si](ivan.porupski@ijs.si) , [branimir.dropuljic@fer.hr](branimir.dropuljic@fer.hr) , nikola.ljubesic@ijs.si arXiv :2607 .05964v 1 [ cs .CL] 7 Jul 2026 Abstract Filled pauses (FPs) are a universal feature of spontaneous speech, yet most studies rely on small, single-language corpora, limiting the generalisability of their findings. We analyse ∼4,000 hours of parliamentary speech across four related Slavic languages (Croatian, Czech, Polish, Serbian) . FP occurrence is obtained via transformer-based automatic detection, while FP rate is modelled using Generalised Estimating Equations (GEE) with Mundlak correction to distinguish within-from betweenspeaker effects. We replicate a negative association of age and speech rate with FP rate, but find that gender effects are language-specific and directionally opposite to most prior literature. Novel analyses of sentiment, political orientation, and power status reveal a consistent positive association between sentiment and FP rate, alongside parliament-specific modulation by orientation and power status, with opposition speakers tending toward lower FP rates than governing coalition speakers. Index Terms: filled pauses, speaker traits, parliamentary speeches, computational paralinguistics 1. Introduction Filled pauses—vocalisations such as uh and um—are a pervasive feature of spontaneous speech, serving functions ranging from turn-holding to signalling lexical difficulty [1] . Despite sustained research interest, most empirical work relies on small, single-language corpora, which limits both statistical power and the generalisability of conclusions across linguistic and cultural contexts. We address this gap by analysing FP production across almost 4,000 hours of parliamentary speech from four related Slavic languages, using a transformer-based detector for automatic FP identification at scale. Parliamentary speech offers a controlled domain with rich speaker metadata, enabling investigation of variables rarely studied together: gender, age, speech rate, expressed sentiment, political orientation, and power status. Our analyses are partly confirmatory—we revisit established predictors (gender, age, speech rate) in a larger and more diverse setting than typical—and partly exploratory, examining sentiment, political orientation, and power status as novel predictors of FP rate. Crucially, we apply Mundlak-corrected GEE models to decompose predictors into stable between-speaker tendencies and utterance-level within-speaker variation, allowing us to distinguish trait-level from state-level associations. **indicates the corresponding author. 2. Related Work 2.1. Automatic FP Identification Early filled-pause detection relied on hand-crafted acoustic cues (pitch/F0, MFCCs, formants, spectral/vocal-tract stability) with modest precision and recall [2, 3, 4] . Prosodic-feature methods improved results (0.61 F1), and prosodic discontinuity features reached 0.83 F1 in spontaneous speech [5, 6] . Recent automation increasingly uses speech transformers (e.g., wav2vec2, HuBERT, WavLM), reaching strong frame-level F1 (0.86-0.88) on Switchboard—a prominent general-population speech benchmark [7, 8] . Recently, a study using wav2vec2-bert, Slovenian training data, and Croatian, Czech, Polish and Serbian parliamentary data for testing, showed very strong cross-lingual performance, with event-level F1 performance of 0.87-0.94 . This performance significantly sur

FAQs

为什么填充停顿（filled pauses, FPs）的研究需要更大规模的跨语言数据？

现有研究多依赖小规模、单语言语料，导致统计力量不足且难以将结论推广到不同语言与文化情境。本文通过分析约4,000小时的跨四种斯拉夫语言议会语料来弥补这一缺口。

本文如何自动识别并统计填充停顿的出现？

使用基于transformers的自动检测器来进行FP识别，并据此获得填充停顿的发生情况。随后用带Mundlak校正的GEE模型对FP率进行建模。

年龄、语速、性别以及情感等因素与FP率的关系有什么主要发现？

年龄与语速与FP率呈负相关；性别效应在不同语言中存在语言特异性，且方向与以往多数研究相反。情感、政治取向和权力状态的分析显示：情感与FP率呈稳定正相关，并且权力地位与取向会对这种关系产生议会特定的调制。

Jul 16, 2026

Upload by Levi