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Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection
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- Citation Author(s):
- Submitted by:
- Quan Wang
- Last updated:
- 5 May 2022 - 10:58am
- Document Type:
- Presentation Slides
- Document Year:
- 2022
- Event:
- Presenters:
- Quan Wang
- Paper Code:
- SPE-72.1
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In this paper, we present a novel speaker diarization system for streaming on-device applications. In this system, we use a transformer transducer to detect the speaker turns, represent each speaker turn by a speaker embedding, then cluster these embeddings with constraints from the detected speaker turns. Compared with conventional clustering-based diarization systems, our system largely reduces the computational cost of clustering due to the sparsity of speaker turns. Unlike other supervised speaker diarization systems which require annotations of time-stamped speaker labels for training, our system only requires including speaker turn tokens during the transcribing process, which largely reduces the human efforts involved in data collection.