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arxiv_cl 85% Match Dataset Paper Linguists,Speech Researchers,NLP Developers,Computational Linguists,Political Scientists 1 day ago

ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbian

speech-audio β€Ί speech-recognition
πŸ“„ Abstract

Abstract: ParlaSpeech is a collection of spoken parliamentary corpora currently spanning four Slavic languages - Croatian, Czech, Polish and Serbian - all together 6 thousand hours in size. The corpora were built in an automatic fashion from the ParlaMint transcripts and their corresponding metadata, which were aligned to the speech recordings of each corresponding parliament. In this release of the dataset, each of the corpora is significantly enriched with various automatic annotation layers. The textual modality of all four corpora has been enriched with linguistic annotations and sentiment predictions. Similar to that, their spoken modality has been automatically enriched with occurrences of filled pauses, the most frequent disfluency in typical speech. Two out of the four languages have been additionally enriched with detailed word- and grapheme-level alignments, and the automatic annotation of the position of primary stress in multisyllabic words. With these enrichments, the usefulness of the underlying corpora has been drastically increased for downstream research across multiple disciplines, which we showcase through an analysis of acoustic correlates of sentiment. All the corpora are made available for download in JSONL and TextGrid formats, as well as for search through a concordancer.
Authors (4)
Nikola Ljubeőić
Peter Rupnik
Ivan Porupski
Taja Kuzman PungerΕ‘ek
Submitted
November 3, 2025
arXiv Category
cs.CL
arXiv PDF

Key Contributions

This paper presents ParlaSpeech 3.0, a significantly enriched collection of spoken parliamentary corpora for Croatian, Czech, Polish, and Serbian, totaling 6,000 hours. The corpora are automatically built and enriched with extensive annotation layers, including linguistic features, sentiment predictions, disfluency occurrences, and detailed word/grapheme-level alignments, making them highly valuable for speech and language research.

Business Value

Provides foundational data for developing improved speech recognition, translation, and analysis tools for Slavic languages, potentially opening new markets for speech technology companies.