Paper Info
Title
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Abstract
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown. To cope with this, we extend an iterative speech extraction system with mechanisms to count the number of sources and combine it with a single-talker speech recognizer to form the first end-to-end multi-talker automatic speech recognition system for an unknown number of active speakers. Our experiments show very promising performance in counting accuracy, source separation and speech recognition on simulated clean mixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a new state-of-the-art word error rate on the WSJ0-2mix database. Furthermore, our system generalizes well to a larger number of speakers than it ever saw during training, as shown in experiments with the WSJ0-4mix database.
Year
DOI
Venue
2020
10.21437/Interspeech.2020-2519
INTERSPEECH
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Thilo von Neumann162.57
Boeddeker Christoph233.84
Lukas Drude39511.10
Keisuke Kinoshita449454.81
Marc Delcroix569962.07
Tomohiro Nakatani61327139.18
Reinhold Haeb-Umbach71487211.71