Abstract—Speaker verification involves examining the speech signal to authenticate the claim of a speaker as true or false. Deep neural networks are one of the successful implementation of complex non-linear models to learn unique and invariant features of data. They have been employed in speech recognition tasks and have shown their potential to be used for speaker recognition also. In this study, we investigate and review Deep Neural Network (DNN) techniques used in speaker verification systems. DNN are used from extracting features to complete end-to-end system for speaker verification. They are generally used to extract speaker-specific representations, for which the network is trained using speaker data in training phase. Speaker representation depends on the type of the model, the representation level, and the model training loss. Usually deep learning is crux of attention in computer vision community for various tasks and we believe that a comprehensive review of current state-of-the-art in deep learning for speaker verification summarize the utilization of these approaches for readers in speech processing community.
Index Terms—Feature extraction, bottleneck features, deep features, end-to-end systems.
The authors are with School of Electrical Engineering and Computer Sciences (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan (e-mail: amna.irum@seecs.edu.pk, ahmad.salman@seecs.edu.pk).
Cite: Amna Irum and Ahmad Salman, "Speaker Verification Using Deep Neural Networks: A Review," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 20-25, 2019.