Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection
Wei Xia, Lu Han, Quan Wang, Anshuman Tripathi, Yiling Huang, Ignacio López Moreno, Haşim Sak
Abstract
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.