19 citations · 49 across the 31 of their papers we have counts for
5 papers · 1 filter
Speech separation with large-scale self-supervised learning
Zhuo Chen, Naoyuki Kanda, Jian Wu +6
Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the ex…
Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation
Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka
Personalized speech enhancement (PSE) models achieve promising results compared with unconditional speech enhancement models due to their ability to remove interfering speech in ad…
Real-Time Joint Personalized Speech Enhancement and Acoustic Echo Cancellation
Sefik Emre Eskimez, Takuya Yoshioka, Alex Ju +3
Personalized speech enhancement (PSE) is a real-time SE approach utilizing a speaker embedding of a target person to remove background noise, reverberation, and interfering voices.…
Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation
Xiaofei Wang, Dongmei Wang, Naoyuki Kanda +2
Existing multi-channel continuous speech separation (CSS) models are heavily dependent on supervised data - either simulated data which causes data mismatch between the training an…
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation
Manthan Thakker, Sefik Emre Eskimez, Takuya Yoshioka +1
This paper investigates how to improve the runtime speed of personalized speech enhancement (PSE) networks while maintaining the model quality. Our approach includes two aspects: a…