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.

通过使用弱监督方法构建大规模语料库，本研究在语音技术方面证实了通过增加数据量和模型规模来提高 ASR 性能的成效，进一步推动了资源匮乏语言的语音技术发展。