DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
Table 2: CER (%) results of multilingual ASR using DARTS-ASR under different fine-tuning approaches.
['Language', 'Fine-tuning of DARTS-ASR Adapt', 'Fine-tuning of DARTS-ASR Adapt', 'Fine-tuning of DARTS-ASR Adapt pruned']
Especially for “Adapt pruned arch.+params”, the architecture was pruned by removing all transformations but the top three ones with the highest {αfi,j} values in each edge. Then the pruned {αi,j} kept being fine-tuned jointly with remaining parameter weights. fine-tuning approach obtained the best performance on average CER. However, “Adapt only param.” and “Adapt pruned arch.+param.” were only a little worse than “Adapt arch.+param.”. It indicates after pre-training, DARTS-ASR can find a generally good architecture and parameter weights for different languages. And the pruned architecture can reduce computational cost while suffering little performance drop. We used “Adapt arch.+param.” fine-tuning approach for DARTS-ASR in the following experiments.