emoDARTS: Joint Optimisation of CNN & Sequential Neural Network
  Architectures for Superior Speech Emotion Recognition
Thejan Rajapakshe Rajib Rana Sara Khalifa Berrak Sisman Bjorn W. Schuller Carlos Busso
Speech Emotion Recognition (SER) is crucial for enabling computers to
understand the emotions conveyed in human communication. With recent
advancements in Deep Learning (DL), the performance of SER models has
significantly improved. However, designing an optimal DL architecture requires
specialised knowledge and experimental assessments. Fortunately, Neural
Architecture Search (NAS) provides a potential solution for automatically
determining the best DL model. The Differentiable Architecture Search (DARTS)
is a particularly efficient method for discovering optimal models. This study
presents emoDARTS, a DARTS-optimised joint CNN and Sequential Neural Network
(SeqNN: LSTM, RNN) architecture that enhances SER performance. The literature
supports the selection of CNN and LSTM coupling to improve performance.
  While DARTS has previously been used to choose CNN and LSTM operations
independently, our technique adds a novel mechanism for selecting CNN and SeqNN
operations in conjunction using DARTS. Unlike earlier work, we do not impose
limits on the layer order of the CNN. Instead, we let DARTS choose the best
layer order inside the DARTS cell. We demonstrate that emoDARTS outperforms
conventionally designed CNN-LSTM models and surpasses the best-reported SER
results achieved through DARTS on CNN-LSTM by evaluating our approach on the
IEMOCAP, MSP-IMPROV, and MSP-Podcast datasets.