17 citations · 104 across the 36 of their papers we have counts for
9 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…
Simulating realistic speech overlaps improves multi-talker ASR
Muqiao Yang, Naoyuki Kanda, Xiaofei Wang +5
Multi-talker automatic speech recognition (ASR) has been studied to generate transcriptions of natural conversation including overlapping speech of multiple speakers. Due to the di…
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
Zili Huang, Zhuo Chen, Naoyuki Kanda +6
Self-supervised learning (SSL), which utilizes the input data itself for representation learning, has achieved state-of-the-art results for various downstream speech tasks. However…
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts
Xiaofei Wang, Zhuo Chen, Yu Shi +3
Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs. First, while a larger model impr…
VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition
Naoyuki Kanda, Jian Wu, Xiaofei Wang +3
This paper presents a novel streaming automatic speech recognition (ASR) framework for multi-talker overlapping speech captured by a distant microphone array with an arbitrary geom…
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…