End-to-end Multichannel Speaker-Attributed ASR: Speaker Guided Decoder
  and Input Feature Analysis
Can Cui Imran Ahamad Sheikh Mostafa Sadeghi Emmanuel Vincent
We present an end-to-end multichannel speaker-attributed automatic speech
recognition (MC-SA-ASR) system that combines a Conformer-based encoder with
multi-frame crosschannel attention and a speaker-attributed Transformer-based
decoder. To the best of our knowledge, this is the first model that efficiently
integrates ASR and speaker identification modules in a multichannel setting. On
simulated mixtures of LibriSpeech data, our system reduces the word error rate
(WER) by up to 12% and 16% relative compared to previously proposed
single-channel and multichannel approaches, respectively. Furthermore, we
investigate the impact of different input features, including multichannel
magnitude and phase information, on the ASR performance. Finally, our
experiments on the AMI corpus confirm the effectiveness of our system for
real-world multichannel meeting transcription.