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

arXiv: https://arxiv.org/abs/2607.05964 · HTML: https://mlsanity.com/paper/2607.05964/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter](https://mlsanity.com/paper/2102.12394/) (2021)
- [Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0](https://mlsanity.com/paper/2204.03417/) (2022)
- [KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering](https://mlsanity.com/paper/2203.05383/) (2022)
- [The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings](https://mlsanity.com/paper/2309.09783/) (2023)
- [Automatic Disfluency Detection from Untranscribed Speech](https://mlsanity.com/paper/2311.00867/) (2023)
- [The Power of Prosody and Prosody of Power: An Acoustic Analysis of Finnish Parliamentary Speech](https://mlsanity.com/paper/2305.16040/) (2023)
- [Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification](https://mlsanity.com/paper/2508.03181/) (2025)
- [ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbian](https://mlsanity.com/paper/2511.01619/) (2025)

## Built on by (most cited first, 1 of 1)

- [Opinionated, Hesitant and Stressed: Three Studies of How Politicians Speak in Four Slavic Parliaments](https://mlsanity.com/paper/2608.30828/) (2026)

## Abstract (arXiv)

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 between- speaker 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.