A novel RBFNN-CNN model for speaker identification in stressful talking environments

Nassif, A ORCID: https://orcid.org/0000-0003-1570-0897, Alnazzawi, N ORCID: https://orcid.org/0000-0003-3265-0297, Shahin, I, Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981, Hindawi, N, Lataifeh, M and Elnagar, A 2022, 'A novel RBFNN-CNN model for speaker identification in stressful talking environments' , Applied Sciences, 12 (10) .

PDF - Published Version
Available under License Creative Commons Attribution 4.0.

Download (1MB) | Preview


Speaker identification systems perform almost ideally in neutral talking environments. However, these systems perform poorly in stressful talking environments. In this paper, we present an effective approach for enhancing the performance of speaker identification in stressful talking environments based on a novel radial basis function neural network-convolutional neural network (RBFNN-CNN) model. In this research, we applied our approach to two distinct speech databases: a local Arabic Emirati-accent dataset and a global English Speech Under Simulated and Actual Stress (SUSAS) corpus. To the best of our knowledge, this is the first work that addresses the use of an RBFNN-CNN model in speaker identification under stressful talking environments. Our speech identification models select the finest speech signal representation through the use of Mel-frequency cepstral coefficients (MFCCs) as a feature extraction method. A comparison among traditional classifiers such as support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors algorithm (KNN) and deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), was conducted. The results of our experiments show that speaker identification performance in stressful environments based on the RBFNN-CNN model is higher than that with the classical and deep machine learning models.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Applied Sciences
Publisher: MDPI
ISSN: 2076-3417
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 29 Jul 2022 05:28
Last Modified: 17 Aug 2022 08:32
URI: https://usir.salford.ac.uk/id/eprint/63927

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year