  Deep neural networks have brought significant advancements to speech emotion
recognition (SER). However, the architecture design in SER is mainly based on
expert knowledge and empirical (trial-and-error) evaluations, which is
time-consuming and resource intensive. In this paper, we propose to apply
neural architecture search (NAS) techniques to automatically configure the SER
models. To accelerate the candidate architecture optimization, we propose a
uniform path dropout strategy to encourage all candidate architecture
operations to be equally optimized. Experimental results of two different
neural structures on IEMOCAP show that NAS can improve SER performance (54.89\%
to 56.28\%) while maintaining model parameter sizes. The proposed dropout
strategy also shows superiority over the previous approaches.
