  Multi-talker conversational speech processing has drawn many interests for
various applications such as meeting transcription. Speech separation is often
required to handle overlapped speech that is commonly observed in conversation.
Although the original utterancelevel permutation invariant training-based
continuous speech separation approach has proven to be effective in various
conditions, it lacks the ability to leverage the long-span relationship of
utterances and is computationally inefficient due to the highly overlapped
sliding windows. To overcome these drawbacks, we propose a novel training
scheme named Group-PIT, which allows direct training of the speech separation
models on the long-form speech with a low computational cost for label
assignment. Two different speech separation approaches with Group-PIT are
explored, including direct long-span speech separation and short-span speech
separation with long-span tracking. The experiments on the simulated
meeting-style data demonstrate the effectiveness of our proposed approaches,
especially in dealing with a very long speech input.
