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This paper presents Transcribe-to-Diarize, a new approach for neural speakerdiarization that uses an end-to-end (E2E) speaker-attributed automatic speechrecognition (SA-ASR). The E2E SA-ASR is a joint model that was recentlyproposed for speaker counting, multi-talker speech recognition, and speakeridentification from monaural audio that contains overlapping speech. Althoughthe E2E SA-ASR model originally does not estimate any time-related information,we show that the start and end times of each word can be estimated withsufficient accuracy from the internal state of the E2E SA-ASR by adding a smallnumber of learnable parameters. Similar to the target-speaker voice activitydetection (TS-VAD)-based diarization method, the E2E SA-ASR model is applied toestimate speech activity of each speaker while it has the advantages of (i)handling unlimited number of speakers, (ii) leveraging linguistic informationfor speaker diarization, and (iii) simultaneously generating speaker-attributedtranscriptions. Experimental results on the LibriCSS and AMI corpora show thatthe proposed method achieves significantly better diarization error rate thanvarious existing speaker diarization methods when the number of speakers isunknown, and achieves a comparable performance to TS-VAD when the number ofspeakers is given in advance. The proposed method simultaneously generatesspeaker-attributed transcription with state-of-the-art accuracy.

Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR