DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
Table 1: CER (%) results of monolingual ASR using different CNN modules.
['Language', 'CNN Module VGG-', 'CNN Module VGG-', 'CNN Module DARTS-ASR', 'CNN Module DARTS-ASR']
The results of DARTS-ASR using all the seven kinds of transformations mentioned in the last subsection are listed in the third column. We can observe DARTS-ASR significantly outperformed both VGG-Small and VGG-Large, showing that the connection pattern of nodes in DARTS-ASR contributed a lot to the huge performance boosting. It is worth noting that even though the parameter size of VGG-Large was four times as many as VGG-Small, the CERs of Vietnamese and Tamil became worse due to overfitting and the CERs of Swahili and Kurmanji improved only a little. In comparison, the parameter size of DARTS-ASR was also much larger than VGG-Small. However, DARTS-ASR outperformed VGG-Small by 10.2% relative reduction on average CER. It indicates the role of architecture for training DNN is very important.