arXiv:2607.05964v1 Announce Type: new 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 between- speaker effects. We replicate a negative association of a
The paper is a new academic publication leveraging transformer models for linguistic analysis, indicating ongoing research in computational linguistics rather than an immediate market or geopolitical event.
This research contributes to the academic understanding of language patterns and the application of AI in linguistics but holds no direct strategic relevance for a sophisticated reader focused on broader trends.
No immediate or significant changes are indicated; this represents incremental academic advancement in natural language processing and linguistics.
Improved methodologies for linguistic analysis using AI.
Better understanding of speech patterns in different languages.
Potentially more nuanced AI speech recognition and generation in the long term, impacting niche applications.
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