most citedSpeech Separation with Pretrained Frontend to Minimize Domain Mismatch

11 citations · 11 across the 3 of their papers we have counts for

collaborators

6 papers

eess.AS2025

Leveraging Language Information for Target Language Extraction

Mehmet Sinan Yıldırım, Ruijie Tao, Wupeng Wang +2

Target Language Extraction aims to extract speech in a specific language from a mixture waveform that contains multiple speakers speaking different languages. The human auditory sy…

cs.CL2025

Fun-ASR Technical Report

Keyu An, Yanni Chen, Zhigao Chen +35

In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep in…

eess.AS2025

Online Audio-Visual Autoregressive Speaker Extraction

Zexu Pan, Wupeng Wang, Shengkui Zhao +4

This paper proposes a novel online audio-visual speaker extraction model. In the streaming regime, most studies optimize the audio network only, leaving the visual frontend less ex…

cs.SD2025

Causal Self-supervised Pretrained Frontend with Predictive Code for Speech Separation

Wupeng Wang, Zexu Pan, Xinke Li +2

Speech separation (SS) seeks to disentangle a multi-talker speech mixture into single-talker speech streams. Although SS can be generally achieved using offline methods, such a pro…

cs.SD2025

Context-Aware Two-Step Training Scheme for Domain Invariant Speech Separation

Wupeng Wang, Zexu Pan, Jingru Lin +2

Speech separation seeks to isolate individual speech signals from a multi-talk speech mixture. Despite much progress, a system well-trained on synthetic data often experiences perf…

cs.SD202411 cited

Speech Separation with Pretrained Frontend to Minimize Domain Mismatch

Wupeng Wang, Zexu Pan, Xinke Li +2

Speech separation seeks to separate individual speech signals from a speech mixture. Typically, most separation models are trained on synthetic data due to the unavailability of ta…