  Streaming recognition of multi-talker conversations has so far been evaluated
only for 2-speaker single-turn sessions. In this paper, we investigate it for
multi-turn meetings containing multiple speakers using the Streaming Unmixing
and Recognition Transducer (SURT) model, and show that naively extending the
single-turn model to this harder setting incurs a performance penalty. As a
solution, we propose the dual-path (DP) modeling strategy first used for
time-domain speech separation. We experiment with LSTM and Transformer based DP
models, and show that they improve word error rate (WER) performance while
yielding faster convergence. We also explore training strategies such as chunk
width randomization and curriculum learning for these models, and demonstrate
their importance through ablation studies. Finally, we evaluate our models on
the LibriCSS meeting data, where they perform competitively with offline
separation-based methods.
