19 citations · 30 across the 12 of their papers we have counts for
13 papers
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…
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…
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka +3
With the recent surge of video conferencing tools usage, providing high-quality speech signals and accurate captions have become essential to conduct day-to-day business or connect…
Personalized Speech Enhancement: New Models and Comprehensive Evaluation
Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang +3
Personalized speech enhancement (PSE) models utilize additional cues, such as speaker embeddings like d-vectors, to remove background noise and interfering speech in real-time and…