mlsanityData to 14 Oct 2026

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

arXiv 2607.05964 ↗First posted 7 Jul 2026cs.CL stat.APcited by 1

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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.

Key facts. Cited by 1 paper 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

8 of 39 references.

References · 8

  1. 2021SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stuttercited by 52
  2. 2022Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0cited by 27
  3. 2022KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stutteringcited by 21
  4. 2023The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedingscited by 6
  5. 2023Automatic Disfluency Detection from Untranscribed Speechcited by 6
  6. 2023The Power of Prosody and Prosody of Power: An Acoustic Analysis of Finnish Parliamentary Speechcited by 1
  7. 2025Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classificationcited by 1
  8. 2025ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbiancited by 1

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