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

arXiv: https://arxiv.org/abs/2511.01619 · HTML: https://mlsanity.com/paper/2511.01619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech](https://mlsanity.com/paper/2111.10367/) (2021)
- [SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter](https://mlsanity.com/paper/2102.12394/) (2021)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering](https://mlsanity.com/paper/2203.05383/) (2022)
- [NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations](https://mlsanity.com/paper/2508.04195/) (2025)
- [FT Speech: Danish Parliament Speech Corpus](https://mlsanity.com/paper/2005.12368/) (2020)
- [CLASSLA-Stanza: The Next Step for Linguistic Processing of South Slavic Languages](https://mlsanity.com/paper/2308.04255/) (2023)
- [The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings](https://mlsanity.com/paper/2409.15397/) (2024)
- [DisMo: A Morphosyntactic, Disfluency and Multi-Word Unit Annotator. An Evaluation on a Corpus of French Spontaneous and Read Speech](https://mlsanity.com/paper/1802.02926/) (2018)
- [Identifying Primary Stress Across Related Languages and Dialects with Transformer-based Speech Encoder Models](https://mlsanity.com/paper/2505.24571/) (2025)
- [How Does a Deep Neural Network Look at Lexical Stress in English Words?](https://mlsanity.com/paper/2508.07229/) (2025)

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

- [Umm... With Transformers? Insights from Filled Pause Use across Four Slavic Parliaments](https://mlsanity.com/paper/2607.05964/) (2026)

## Abstract (arXiv)

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.