The Greek podcast corpus: Competitive speech models for low-resourced languages with weakly supervised data
This work addresses the challenge of developing speech recognition for under-resourced languages like Greek, though it is incremental as it applies existing weak supervision methods to a new dataset.
The researchers tackled the problem of limited data for speech technologies in low-resourced languages by compiling an 800-hour Greek podcast corpus with silver transcriptions, which led to consistent word error rate (WER) improvements when fine-tuning models.
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.