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20212026
most citedSelf-Supervised Learning Based Domain Adaptation for Robust Speaker Verification

41 citations · 104 across the 27 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.SD2024★ 4 cited

Data-Efficient Low-Complexity Acoustic Scene Classification via Distilling and Progressive Pruning

Bing Han, Wen Huang, Zhengyang Chen +7

The goal of the acoustic scene classification (ASC) task is to classify recordings into one of the predefined acoustic scene classes. However, in real-world scenarios, ASC systems…

eess.AS2024

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…

cs.SD2024

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…

cs.SD2024

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,…

cs.SD2024

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…

eess.AS2024★ 1 cited

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…