CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus
Table 3: WER and CER scores for ASR models. Non-English models are pretrained using English model’s encoder.
['[EMPTY]', 'CoVoST Test WER', 'CoVoST Test CER', 'TT WER', 'TT CER']
For simplicity, we use the same model architecture for ASR and ST. We see that French and German perform the best given they are the two highest resource languages in CoVoST. Italian is among the best as well, which is mid-resource and has limited accents. Persian is also mid-resource but is challenging because of rich speaker diversity. Most of the other languages are low resource (especially Swedish and Mongolian) and the ASR models are having difficulties to learn from this data even with pre-trained encoders.