# The Greek podcast corpus: Competitive speech models for low-resourced languages with weakly supervised data

arXiv: https://arxiv.org/abs/2406.15284 · HTML: https://mlsanity.com/paper/2406.15284/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Self-Training for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.09116/) (2019)
- [Powerset multi-class cross entropy loss for neural speaker diarization](https://mlsanity.com/paper/2310.13025/) (2023)
- [The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage](https://mlsanity.com/paper/2111.09344/) (2021)
- [Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://mlsanity.com/paper/2311.00430/) (2023)
- [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://mlsanity.com/paper/1903.11269/) (2019)
- [Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek](https://mlsanity.com/paper/2301.00304/) (2022)
- [A Novel Self-training Approach for Low-resource Speech Recognition](https://mlsanity.com/paper/2308.05269/) (2023)
- [Cem Mil Podcasts: A Spoken Portuguese Document Corpus For Multi-modal, Multi-lingual and Multi-Dialect Information Access Research](https://mlsanity.com/paper/2209.11871/) (2022)

## Built on by (most cited first, 4 of 4)

- [MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR](https://mlsanity.com/paper/2505.24656/) (2025)
- [VOX-KRIKRI: Unifying Speech and Language through Continuous Fusion](https://mlsanity.com/paper/2509.15667/) (2025)
- [Automatic Lyric Transcription for Greek Songs: Scaling and Task Composition Effects in Whisper Adaptation](https://mlsanity.com/paper/2609.11302/) (2026)
- [Pass or Fail? Evaluating LLMs on Two Greek Examination Benchmarks](https://mlsanity.com/paper/2609.34800/) (2026)

## Abstract (arXiv)

The development of speech technologies for languages with limited digital representation poses significant challenges, primarily due to the scarcity of available data. This issue is exacerbated in the era of large, data-intensive models. Recent research has underscored the potential of leveraging weak supervision to augment the pool of available data. In this study, we compile an 800-hour corpus of Modern Greek from podcasts and employ Whisper large-v3 to generate silver transcriptions. This corpus is utilized to fine-tune our models, aiming to assess the efficacy of this approach in enhancing ASR performance. Our analysis spans 16 distinct podcast domains, alongside evaluations on established datasets for Modern Greek. The findings indicate consistent WER improvements, correlating with increases in both data volume and model size. Our study confirms that assembling large, weakly supervised corpora serves as a cost-effective strategy for advancing speech technologies in under-resourced languages.