# Finnish Parliament ASR corpus - Analysis, benchmarks and statistics

arXiv: https://arxiv.org/abs/2203.14876 · HTML: https://mlsanity.com/paper/2203.14876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates](https://mlsanity.com/paper/1911.03167/) (2019)
- [A comparison of end-to-end models for long-form speech recognition](https://mlsanity.com/paper/1911.02242/) (2019)
- [PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR](https://mlsanity.com/paper/2005.09824/) (2020)
- [Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpus](https://mlsanity.com/paper/2010.02810/) (2020)
- [FT Speech: Danish Parliament Speech Corpus](https://mlsanity.com/paper/2005.12368/) (2020)
- [The Norwegian Parliamentary Speech Corpus](https://mlsanity.com/paper/2201.10881/) (2022)
- [Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks](https://mlsanity.com/paper/2203.12906/) (2022)
- [Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies](https://mlsanity.com/paper/1707.04227/) (2017)
- [Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances](https://mlsanity.com/paper/2002.05150/) (2020)

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

- [EuroSpeech: A Multilingual Speech Corpus](https://mlsanity.com/paper/2510.00514/) (2025)

## Abstract (arXiv)

Public sources like parliament meeting recordings and transcripts provide ever-growing material for the training and evaluation of automatic speech recognition (ASR) systems. In this paper, we publish and analyse the Finnish parliament ASR corpus, the largest publicly available collection of manually transcribed speech data for Finnish with over 3000 hours of speech and 449 speakers for which it provides rich demographic metadata. This corpus builds on earlier initial work, and as a result the corpus has a natural split into two training subsets from two periods of time. Similarly, there are two official, corrected test sets covering different times, setting an ASR task with longitudinal distribution-shift characteristics. An official development set is also provided. We develop a complete Kaldi-based data preparation pipeline, and hidden Markov model (HMM), hybrid deep neural network (HMM-DNN) and attention-based encoder-decoder (AED) ASR recipes. We set benchmarks on the official test sets, as well as multiple other recently used test sets. Both temporal corpus subsets are already large, and we observe that beyond their scale, ASR performance on the official test sets plateaus, whereas other domains benefit from added data. The HMM-DNN and AED approaches are compared in a carefully matched equal data setting, with the HMM-DNN system consistently performing better. Finally, the variation of the ASR accuracy is compared between the speaker categories available in the parliament metadata to detect potential biases based on factors such as gender, age, and education.