Core Concepts
Optimizing joint CNN and SeqNN architectures using DARTS enhances SER performance.
Abstract
The article introduces emoDARTS, a DARTS-optimized joint CNN and SeqNN architecture for improved Speech Emotion Recognition (SER). It discusses the challenges in designing optimal DL architectures for SER and the potential of Neural Architecture Search (NAS) to automate this process. The Differentiable Architecture Search (DARTS) method is highlighted as an efficient approach. The study demonstrates that emoDARTS outperforms conventional models by allowing DARTS to select optimal configurations without constraints on layer order.
Structure:
Introduction to SER advancements with DL.
Overview of NAS and DARTS.
Introduction of emoDARTS architecture.
Comparison with conventional models and previous studies.
Experimental setup with datasets and features.
Evaluation results comparing emoDARTS with baseline models.
Restricting search scope for SeqNN component.
Challenges faced and strategies employed.
Stats
"The Differentiable Architecture Search (DARTS) is a particularly efficient method for discovering optimal models."
"emoDARTS outperforms conventionally designed CNN-LSTM models."
"Experimental results demonstrate that emoDARTS achieves considerably higher SER accuracy than humans designing the CNN-LSTM configuration."
Quotes
"The literature supports the selection of CNN and LSTM coupling to improve performance."
"We demonstrate that emoDARTS outperforms conventionally designed CNN-LSTM models."