From the 1 of 5 linked papers with an AI index.
5 papers
SLT 2026 REAL-TSE Challenge: Real-world Target Speaker Extraction from Conversational Recordings
Shuai Wang, Zihan Qian, Ke Zhang +9
The paper presents the REAL‑TSE Challenge, a benchmark for extracting a target speaker’s voice from real conversational recordings in Mandarin and English, with both online low‑lat…
G-MaP-SE: Guided Speech Enhancement via GMM-Based Prior Matching
Yike Zhu, Ziqian Wang, Zikai Liu +5
Using speaker embeddings as conditioning can strengthen speech enhancement, but most methods either require clean enrollment audio or rely on embeddings extracted from noisy speech…
EvoTSE: Evolving Enrollment for Target Speaker Extraction
Zikai Liu, Ziqian Wang, Xingchen Li +4
Target Speaker Extraction (TSE) aims to isolate a specific speaker's voice from a mixture, guided by a pre-recorded enrollment. While TSE bypasses the global permutation ambiguity…
UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling
Ziqian Wang, Zikai Liu, Yike Zhu +6
Generative modeling has recently achieved remarkable success across image, video, and audio domains, demonstrating powerful capabilities for unified representation learning. Yet sp…
FlowSE: Efficient and High-Quality Speech Enhancement via Flow Matching
Ziqian Wang, Zikai Liu, Xinfa Zhu +6
Generative models have excelled in audio tasks using approaches such as language models, diffusion, and flow matching. However, existing generative approaches for speech enhancemen…