Leveraging translations for speech transcription in low-resource settings
Table 1: Multi-Source models achieve lower Character Error Rates (CER) on all three target languages, even in extremely low resource settings (Ainu, Mboshi). In Spanish, we observe an 8.4% reduction in CER.
['Source', 'Transcription CER Ainu', 'Transcription CER Mboshi', 'Transcription CER Spanish']
In the two extremely low-resource datasets, Ainu and Mboshi, we find that the speech-only baselines are quite competitive. The very small number of speakers (one and three, respectively) makes the speech transcription task easier. In contrast, the translation task is much harder with so little data, as is also confirmed by the poor performance of the translation-only models in Ainu and Mboshi.