AAD-Net: Advanced end-to-end signal processing system for human emotion detection & recognition using attention-based deep echo state network

Speech signals are the most convenient way of communication between human beings and the eventual method of Human-Computer Interaction (HCI) to exchange emotions and information. Rec-ognizing emotions from speech signals is a challenging task due to the sparse nature of emotional data and features. In this article, we proposed a Deep Echo-State-Network (DeepESN) system for emotion recognition with a dilated convolution neural network and multi-headed attention mechanism. To reduce the model complexity, we incorporate a DeepESN that combines reservoir computing for higher-dimensional mapping. We also used fine-tuned Sparse Random Projection (SRP) to reduce dimensionality and adopted an early fusion strategy to fuse the extracted cues and passed the joint feature vector via a classification layer to recognize emotions. Our proposed model is evaluated on two public speech corpora, EMO-DB and RAVDESS, and tested for subject/speaker-dependent/independent performance. The results show that our proposed system achieves a high recognition rate, 91.14, 85.57 for EMO-DB, and 82.01, 77.02 for RAVDESS, using speaker-dependent and independent experiments, respectively. Our proposed system outperforms the State-of-The-Art (SOTA) while requiring less computational time.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This article proposes a Deep Echo-State-Network (DeepESN) system for emotion recognition using a dilated convolutional neural network and multi-headed attention mechanism. The proposed system achieves high recognition rates on two public speech corpora, EMO-DB and RAVDESS, outperforming the State-of-The-Art (SOTA). The system also requires less computational time. Rating: 8/10.