Analysis and Investigation of Speaker Identification Problems Using Deep Learning Networks and the YOHO English Speech Dataset

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