Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

In this study, we investigate self-supervised representation learning for speaker verification
(SV). First, we examine a simple contrastive learning approach (SimCLR) with a momentum …

Self-supervised learning (SSL) has drawn an increased attention in the field of speech
processing. Recent studies have demonstrated that contrastive learning is able to learn …

Singing voice conversion is converting the timbre in the source singing to the target
speaker's voice while keeping singing content the same. However, singing data for target …

Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech
communication as well as the front-end speech enhancement for recognition in the …

This paper proposes the target speaker enhancement based speaker verification network
(TASE-SVNet), an all neural model that couples target speaker enhancement and speaker …

Seismologists have to deal with overlapping and noisy signals. Techniques such as source
separation can be used to solve this problem. Over the past few decades, signal processing …

In this paper, a serialized multi-layer multi-head attention is proposed for extracting neural
speaker embedding in text-independent speaker verification task. The majority of the recent …

Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a
mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep …

In speech separation, the identities of the speakers may be an important cue to discriminate
speeches in the mixture and separate them better. A few recent researches used the …