Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments

no code implementations • 26 Dec 2021 • Ismail Shahin, Ali Bou Nassif, Nawel Nemmour, Ashraf Elnagar, Adi Alhudhaif, Kemal Polat

The test results of the aforementioned hybrid models demonstrated that the proposed HMM-DNN leveraged the verification performance in emotional and stressful environments.

Text-Independent Speaker Verification

Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition

no code implementations • 26 Dec 2021 • Ismail Shahin, Noor Hindawi, Ali Bou Nassif, Adi Alhudhaif, Kemal Polat

Using the Arabic Emirati-accented corpus, our results demonstrate that the proposed work yields average emotion recognition accuracy of 89. 3% compared to 84. 7%, 82. 2%, 69. 8%, 69. 2%, 53. 8%, 42. 6%, and 31. 9% based on CapsNet, CNN, support vector machine, multi-layer perceptron, k-nearest neighbor, radial basis function, and naive Bayes, respectively.

Speech Emotion Recognition