Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning
Table 3: Performace of various source tasks used in transfer learning (TL) and meta-learning (ML) approaches.
['ST Task', 'TL ASR (wer ↓)', 'ML ASR (wer ↓)', 'ML MT (bleu ↑)']
The performance of the ASR model used in transfer learning approach is significantly better than the ASR model in meta-learning approach. However, we achieved significantly better results for target ST task with the meta-learning approach. This is expected given that in the meta-learning phase, we update the parameters with a focus to adapt to the target ST task instead of focusing heavily on learning the particular source task. This also applies to the MT model, whose performance is lower than the standard MT model.