Mixture Encoder Supporting Continuous Speech Separation for Meeting
  Recognition
Peter Vieting Simon Berger Thilo von Neumann Christoph Boeddeker Ralf Schlüter Reinhold Haeb-Umbach
Many real-life applications of automatic speech recognition (ASR) require
processing of overlapped speech. A commonmethod involves first separating the
speech into overlap-free streams and then performing ASR on the resulting
signals. Recently, the inclusion of a mixture encoder in the ASR model has been
proposed. This mixture encoder leverages the original overlapped speech to
mitigate the effect of artifacts introduced by the speech separation.
Previously, however, the method only addressed two-speaker scenarios. In this
work, we extend this approach to more natural meeting contexts featuring an
arbitrary number of speakers and dynamic overlaps. We evaluate the performance
using different speech separators, including the powerful TF-GridNet model. Our
experiments show state-of-the-art performance on the LibriCSS dataset and
highlight the advantages of the mixture encoder. Furthermore, they demonstrate
the strong separation of TF-GridNet which largely closes the gap between
previous methods and oracle separation.