51 citations · 52 across the 5 of their papers we have counts for
6 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…
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…
Asteroid: the PyTorch-based audio source separation toolkit for researchers
Manuel Pariente, Samuele Cornell, Joris Cosentino +11
This paper describes Asteroid, the PyTorch-based audio source separation toolkit for researchers. Inspired by the most successful neural source separation systems, it provides all…
The Speed Submission to DIHARD II: Contributions & Lessons Learned
Md Sahidullah, Jose Patino, Samuele Cornell +11
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides describing the system, whic…
SLOGD: Speaker LOcation Guided Deflation approach to speech separation
Sunit Sivasankaran, Emmanuel Vincent, Dominique Fohr
Speech separation is the process of separating multiple speakers from an audio recording. In this work we propose to separate the sources using a Speaker LOcalization Guided Deflat…
Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition
Sunit Sivasankaran, Emmaneul Vincent, Dominique Fohr
We investigate the effect of speaker localization on the performance of speech recognition systems in a multispeaker, multichannel environment. Given the speaker location informati…