  In previous works, only parameter weights of ASR models are optimized under
fixed-topology architecture. However, the design of successful model
architecture has always relied on human experience and intuition. Besides, many
hyperparameters related to model architecture need to be manually tuned.
Therefore in this paper, we propose an ASR approach with efficient
gradient-based architecture search, DARTS-ASR. In order to examine the
generalizability of DARTS-ASR, we apply our approach not only on many languages
to perform monolingual ASR, but also on a multilingual ASR setting. Following
previous works, we conducted experiments on a multilingual dataset, IARPA
BABEL. The experiment results show that our approach outperformed the baseline
fixed-topology architecture by 10.2% and 10.0% relative reduction on character
error rates under monolingual and multilingual ASR settings respectively.
Furthermore, we perform some analysis on the searched architectures by
DARTS-ASR.
