1 citations · 2 across the 12 of their papers we have counts for
12 papers
Prototype and Instance Contrastive Learning for Unsupervised Domain Adaptation in Speaker Verification
Wen Huang, Bing Han, Zhengyang Chen +2
Speaker verification system trained on one domain usually suffers performance degradation when applied to another domain. To address this challenge, researchers commonly use featur…
WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction
Shuai Wang, Ke Zhang, Shaoxiong Lin +6
Target speaker extraction (TSE) focuses on isolating the speech of a specific target speaker from overlapped multi-talker speech, which is a typical setup in the cocktail party pro…
Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching
Zhengyang Chen, Bing Han, Shuai Wang +2
Speaker diarization is typically considered a discriminative task, using discriminative approaches to produce fixed diarization results. In this paper, we explore the use of neural…
Disentangling the Prosody and Semantic Information with Pre-trained Model for In-Context Learning based Zero-Shot Voice Conversion
Zhengyang Chen, Shuai Wang, Mingyang Zhang +3
Voice conversion (VC) aims to modify the speaker's timbre while retaining speech content. Previous approaches have tokenized the outputs from self-supervised into semantic tokens,…
Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning
Shuai Wang, Zhengyang Chen, Kong Aik Lee +2
Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various i…
Generating Speakers by Prompting Listener Impressions for Pre-trained Multi-Speaker Text-to-Speech Systems
Zhengyang Chen, Xuechen Liu, Erica Cooper +2
This paper proposes a speech synthesis system that allows users to specify and control the acoustic characteristics of a speaker by means of prompts describing the speaker's traits…
Target Speech Diarization with Multimodal Prompts
Yidi Jiang, Ruijie Tao, Zhengyang Chen +2
Traditional speaker diarization seeks to detect ``who spoke when'' according to speaker characteristics. Extending to target speech diarization, we detect ``when target event occur…
Prompt-driven Target Speech Diarization
Yidi Jiang, Zhengyang Chen, Ruijie Tao +3
We introduce a novel task named `target speech diarization', which seeks to determine `when target event occurred' within an audio signal. We devise a neural architecture called Pr…
Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
Shuai Wang, Qibing Bai, Qi Liu +5
Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets. To boost the system performance, researchers leverage lar…
Attention-based Encoder-Decoder End-to-End Neural Diarization with Embedding Enhancer
Zhengyang Chen, Bing Han, Shuai Wang +1
Deep neural network-based systems have significantly improved the performance of speaker diarization tasks. However, end-to-end neural diarization (EEND) systems often struggle to…
USED: Universal Speaker Extraction and Diarization
Junyi Ao, Mehmet Sinan Yıldırım, Ruijie Tao +4
Speaker extraction and diarization are two enabling techniques for real-world speech applications. Speaker extraction aims to extract a target speaker's voice from a speech mixture…
Attention-based Encoder-Decoder Network for End-to-End Neural Speaker Diarization with Target Speaker Attractor
Zhengyang Chen, Bing Han, Shuai Wang +1
This paper proposes a novel Attention-based Encoder-Decoder network for End-to-End Neural speaker Diarization (AED-EEND). In AED-EEND system, we incorporate the target speaker enro…