emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition

arXiv:2403.14083v1 Announce Type: cross
Abstract: 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 …

abstract architecture architectures arxiv cnn communication computers cs.lg cs.sd deep learning designing eess.as emotion emotions enabling however human network neural network optimisation performance recognition speech speech emotion type