The rapid momentum of deep neural networks (DNNs) in recent years has yielded state-of-the-art performance in various machine-learning tasks using speaker identification systems. Speaker identification is based on the speech signals and the features that can be extracted from them. In this article, we proposed a speaker identification system using the developed DNNs models. The system is based on the acoustic and prosodic features of the speech signal, such as pitch frequency (vocal cords vibration rate), energy (loudness of speech), their derivations, and any additional acoustic and prosodic features. Additionally, the article investigates the existing recurrent neural networks (RNNs) models and adapts them to design a speaker identification system using the public YOHO LDC dataset. The average accuracy of the system was 91.93% in the best experiment for speaker identification. Furthermore, this paper helps uncover reasons for analyzing speakers and tokens yielding major errors to increase the system’s robustness regarding feature selection and system tune-up.

Nowadays, digital speech processing has become a required field due to the evaluation of communication technology. Speech signals for any speaker hold meaningful information such as identity, gender, and age. Research into the gender and age of speakers has shown remarkable results in using deep learning architectures to capture the time and frequency pattern of the speech signal. Deep learning confers researchers the motivation to investigate speaker classification methods more deeply. In this work, we develop a speaker gender classification system using Bidirectional Long Short-Term Memory (BLSTM). The developed system is based on the prosodic features of the speech signal, such as speech fundamental frequency and log energy of the signal and their derivations. The considered prosodic features of the system give a novel method in the gender classification system. The system adapts using the public YOHO speech dataset and gives an overall accuracy of 98.59%, with 99.39% and 95.96% average accuracy for males and females, respectively. Also, human being perceptual classification is considered for critically missed speakers. This is helpful in order to uncover reasons for high errors for the purpose of increasing the robustness of the system regarding features selection and system tune up.