The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

The availability of smart devices leads to an exponential increase in multimedia content.
However, the rapid advancements in deep learning have given rise to sophisticated …

Audio Deepfake Detection (ADD) aims to detect the fake audio generated by text-to-speech
(TTS), voice conversion (VC) and replay, etc., which is an emerging topic. Traditionally we …

Domain shift poses a significant challenge in speaker verification, especially in open-set
scenarios where the speaker categories are disjoint between the source and target …

Deep speaker embedding extraction models have recently served as the cornerstone for
modular speaker diarization systems. However, in current modular systems, the extracted …

The availability of smart devices leads to an exponential increase in multimedia content.
However, advancements in deep learning have also enabled the creation of highly …

In this paper, we propose a speaker verification method by an Attentive Multi-scale
Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local …

Conventional time-delay neural networks (TDNNs) struggle to handle long-range context,
their ability to represent speaker information is therefore limited for long utterances. Existing …

This paper explores three novel approaches to improve the performance of speaker
verification (SV) systems based on deep neural networks (DNN) using Multi-head Self …

The use of deep neural networks (DNN) has dramatically elevated the performance of
automatic speaker verification (ASV) over the last decade. However, ASV systems can be …