# Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks

arXiv: https://arxiv.org/abs/2203.12906 · HTML: https://mlsanity.com/paper/2203.12906/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [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)
- [Reducing Gender Bias in Abusive Language Detection](https://mlsanity.com/paper/1808.07231/) (2018)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [Recognizing long-form speech using streaming end-to-end models](https://mlsanity.com/paper/1910.11455/) (2019)
- [A comparison of end-to-end models for long-form speech recognition](https://mlsanity.com/paper/1911.02242/) (2019)
- [Finnish Dialect Identification: The Effect of Audio and Text](https://mlsanity.com/paper/2111.03800/) (2021)
- [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, 7 of 7)

- [Speech Analysis of Language Varieties in Italy](https://mlsanity.com/paper/2406.15862/) (2024)
- [Comparison and Analysis of New Curriculum Criteria for End-to-End ASR](https://mlsanity.com/paper/2208.05782/) (2022)
- [Finnish Parliament ASR corpus - Analysis, benchmarks and statistics](https://mlsanity.com/paper/2203.14876/) (2022)
- [Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpus](https://mlsanity.com/paper/2505.17833/) (2025)
- [Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information](https://mlsanity.com/paper/2307.11450/) (2023)
- [Looking for Affect in Spontaneous Finnish Speech through Linguistic Interpretability](https://mlsanity.com/paper/2607.24155/) (2026)
- [From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale](https://mlsanity.com/paper/2610.13102/) (2026)

## Abstract (arXiv)

The Donate Speech campaign has so far succeeded in gathering approximately 3600 hours of ordinary, colloquial Finnish speech into the Lahjoita puhetta (Donate Speech) corpus. The corpus includes over twenty thousand speakers from all the regions of Finland and from all age brackets. The primary goals of the collection were to create a representative, large-scale resource to study spontaneous spoken Finnish and to accelerate the development of language technology and speech-based services. In this paper, we present the collection process and the collected corpus, and showcase its versatility through multiple use cases. The evaluated use cases include: automatic speech recognition of spontaneous speech, detection of age, gender, dialect and topic and metadata analysis. We provide benchmarks for the use cases, as well down loadable, trained baseline systems with open-source code for reproducibility. One further use case is to verify the metadata and transcripts given in this corpus itself, and to suggest artificial metadata and transcripts for the part of the corpus where it is missing.