Electrical Engineering and Systems Science > Audio and Speech Processing
[Submitted on 23 Sep 2021 (v1), last revised 25 Jan 2022 (this version, v3)]
Title:Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection
View PDFAbstract: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.
Submission history
From: Quan Wang [view email][v1] Thu, 23 Sep 2021 20:47:57 UTC (376 KB)
[v2] Tue, 5 Oct 2021 22:53:02 UTC (165 KB)
[v3] Tue, 25 Jan 2022 18:48:38 UTC (165 KB)
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