  This paper presents our latest investigation on end-to-end automatic speech
recognition (ASR) for overlapped speech. We propose to train an end-to-end
system conditioned on speaker embeddings and further improved by transfer
learning from clean speech. This proposed framework does not require any
parallel non-overlapped speech materials and is independent of the number of
speakers. Our experimental results on overlapped speech datasets show that
joint conditioning on speaker embeddings and transfer learning significantly
improves the ASR performance.
