Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …

For speaker recognition, it is difficult to extract an accurate speaker representation from
speech because of its mixture of speaker traits and content. This paper proposes a …

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of
progressively learn the more abstract and composite representations of the raw features of …

The emergence of biometric technology provides enhanced security compared to the
traditional identification and authentication techniques that were less efficient and secure …

End-to-end speaker verification achieves the verification through estimating directly the
similarity score between a pair of utterances, which is formulated as a binary (ie, target …

With the rise in mobile phone users and VoIP, voice has emerged as an easy and
accessible biometric modality for identification or verification tasks. Given the increasing …

Temporal dynamic convolution neural networks (TDY-CNNs) extract speaker embeddings
considering the time-varying characteristics of speech and improve text-independent …

This paper proposes a multi-task learning network with phoneme-aware and channel-wise
attentive learning strategies for text-dependent Speaker Verification (SV). In the proposed …

Deep speaker embeddings have become the leading method for encoding speaker identity
in speaker recognition tasks. The embedding space should ideally capture the variations …

In this paper, we study a novel technique that exploits the interaction between speaker traits
and linguistic content to improve both speaker verification and utterance verification …