Speaker diarization assisted ASR for multi-speaker conversations

In this paper, we propose a novel approach for the transcription of speech conversations with natural speaker overlap, from single channel recordings. We propose a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system with speaker activity assisted acoustic model (AM). An end-to-end neural network system is used for speaker diarization. Two architectures, (i) input conditioned AM, and (ii) gated features AM, are explored to incorporate the speaker activity information. The models output speaker specific senones. The experiments on Switchboard telephone conversations show the advantage of incorporating speaker activity information in the ASR system for recordings with overlapped speech. In particular, an absolute improvement of $11\%$ in word error rate (WER) is seen for the proposed approach on natural conversation speech with automatic diarization.