  This paper presents our recent effort on end-to-end speaker-attributed
automatic speech recognition, which jointly performs speaker counting, speech
recognition and speaker identification for monaural multi-talker audio.
Firstly, we thoroughly update the model architecture that was previously
designed based on a long short-term memory (LSTM)-based attention encoder
decoder by applying transformer architectures. Secondly, we propose a speaker
deduplication mechanism to reduce speaker identification errors in highly
overlapped regions. Experimental results on the LibriSpeechMix dataset shows
that the transformer-based architecture is especially good at counting the
speakers and that the proposed model reduces the speaker-attributed word error
rate by 47% over the LSTM-based baseline. Furthermore, for the LibriCSS
dataset, which consists of real recordings of overlapped speech, the proposed
model achieves concatenated minimum-permutation word error rates of 11.9% and
16.3% with and without target speaker profiles, respectively, both of which are
the state-of-the-art results for LibriCSS with the monaural setting.
