  This paper investigates the use of target-speaker automatic speech
recognition (TS-ASR) for simultaneous speech recognition and speaker
diarization of single-channel dialogue recordings. TS-ASR is a technique to
automatically extract and recognize only the speech of a target speaker given a
short sample utterance of that speaker. One obvious drawback of TS-ASR is that
it cannot be used when the speakers in the recordings are unknown because it
requires a sample of the target speakers in advance of decoding. To remove this
limitation, we propose an iterative method, in which (i) the estimation of
speaker embeddings and (ii) TS-ASR based on the estimated speaker embeddings
are alternately executed. We evaluated the proposed method by using very
challenging dialogue recordings in which the speaker overlap ratio was over
20%. We confirmed that the proposed method significantly reduced both the word
error rate (WER) and diarization error rate (DER). Our proposed method combined
with i-vector speaker embeddings ultimately achieved a WER that differed by
only 2.1 % from that of TS-ASR given oracle speaker embeddings. Furthermore,
our method can solve speaker diarization simultaneously as a by-product and
achieved better DER than that of the conventional clustering-based speaker
diarization method based on i-vector.
