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Most approaches to multi-talker overlapped speech separation and recognitionassume that the number of simultaneously active speakers is given, but inrealistic situations, it is typically unknown. To cope with this, we extend aniterative speech extraction system with mechanisms to count the number ofsources and combine it with a single-talker speech recognizer to form the firstend-to-end multi-talker automatic speech recognition system for an unknownnumber of active speakers. Our experiments show very promising performance incounting accuracy, source separation and speech recognition on simulated cleanmixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a newstate-of-the-art word error rate on the WSJ0-2mix database. Furthermore, oursystem generalizes well to a larger number of speakers than it ever saw duringtraining, as shown in experiments with the WSJ0-4mix database.

Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR