# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
###### Abstract
5 Computer science and engineering Department, Yanbu University College, Royal Commission for Jubail and Yanbu Industrial City, Yanbu Industrial City, Yanbu 46435, Saudi Arabia; [EMAIL_ADDRESS]  
5 Computer science and engineering Departmentspeaker verification involves automatic detection of whether the speaker is a specific person or not. Speaker identification can be used for various purposes. For instance, applications such as credit card security, confidential data safety, client identity and verification are some examples of broad speaker recognition applications. The functioning of speaker identification is either text-dependent [5; 6; 7] or text-independent [8; 9; 10; 11; 12]. Many circumstances can cause stress, including noisy backgrounds, emergencies such as aircraft pilot communications, high workloads, physical environmental factors, and multitasking [13]. There are several applications of speech recognition under stressful conditions in real life. This includes emergency call centers, telephone banking and military voice communications applications. An emotional talking environment occurs when the speaker speaks his or her language under the influence of emotional conditions such as anger, happiness, and sadness. Emotion recognition applications are found in telecommunications, human-robot interfaces, and intelligent call centers. Emotion recognition can be used in intelligent language education systems to sense and adapt to student emotions when they reach a state of boredom during a tutoring session [14; 15]. Speaker identification in emotional and stressful conditions is challenging because of the mismatch between training and testing. In the training stage, the models are trained using neutral conditions; however, in the testing phase, models are tested using neutral, as well as emotional and stressful, conditions. This why models perform better when they are tested under neutral conditions. Speaker identification also has various applications in civil cases and the media. This includes applications in recorded discussions and phone calls made to radio stations or other state and local departments and insurance companies [16]. It is important that the speaker be identified in such cases, since some people tend to disguise their voices to imitate famous people in the media, which may affect their reputation. The main aim of this research was to further enhance speaker identification performance in stressful talking environments using a novel RBFNN-CNN model. In this research, we evaluated our approach with two distinct speech databases: a local Arabic Emirati-accent dataset and a global English Speech Under Simulated and Actual Stress (SUSAS) corpus. Our speech identification models selected the finest speech signal representation by feature extraction such as Mel-frequency cepstral coefficients (MFCCs), after having been processed. We evaluated our proposed model against different classical classifiers such as support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors algorithm (KNN), and different deep learning models such as convolutional neural network (CNN) and recurrent neural network (RNN). Results show that the proposed model outperforms all other models. The remaining sections are organized as follows: Section 2 presents related work. Section 3 describes the datasets used in this research, while the model architecture and methodology are explained in Section 4. The results of the models are presented in Section 5. Section 6 concludes the paper.