  Speech Emotion Recognition (SER) is a critical enabler of emotion-aware
communication in human-computer interactions. Recent advancements in Deep
Learning (DL) have substantially enhanced the performance of SER models through
increased model complexity. However, designing optimal DL architectures
requires prior experience and experimental evaluations. Encouragingly, Neural
Architecture Search (NAS) offers a promising avenue to determine an optimal DL
model automatically. In particular, Differentiable Architecture Search (DARTS)
is an efficient method of using NAS to search for optimised models. This paper
proposes a DARTS-optimised joint CNN and LSTM architecture, to improve SER
performance, where the literature informs the selection of CNN and LSTM
coupling to offer improved performance. While DARTS has previously been applied
to CNN and LSTM combinations, our approach introduces a novel mechanism,
particularly in selecting CNN operations using DARTS. In contrast to previous
studies, we refrain from imposing constraints on the order of the layers for
the CNN within the DARTS cell; instead, we allow DARTS to determine the optimal
layer order autonomously. Experimenting with the IEMOCAP and MSP-IMPROV
datasets, we demonstrate that our proposed methodology achieves significantly
higher SER accuracy than hand-engineering the CNN-LSTM configuration. It also
outperforms the best-reported SER results achieved using DARTS on CNN-LSTM.
