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

TL;DR

This paper demonstrates that large, weakly supervised speech corpora can significantly improve automatic speech recognition for low-resource languages like Greek, using podcast data and modern models.

Contribution

It introduces an 800-hour Greek podcast corpus with weak supervision and evaluates its effectiveness in enhancing ASR models for under-resourced languages.

Findings

WER improvements with increased data volume

Model size correlates with performance gains

Weakly supervised data is cost-effective for low-resource ASR

Abstract

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.