41 citations · 83 across the 5 of their papers we have counts for
9 papers
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
Shota Horiguchi, Shinji Watanabe, Paola Garcia +3
Attractor-based end-to-end diarization is achieving comparable accuracy to the carefully tuned conventional clustering-based methods on challenging datasets. However, the main draw…
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap
Shota Horiguchi, Nelson Yalta, Paola Garcia +7
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of…
Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers
Yawen Xue, Shota Horiguchi, Yusuke Fujita +4
We propose a streaming diarization method based on an end-to-end neural diarization (EEND) model, which handles flexible numbers of speakers and overlapping speech. In our previous…
Neural Speaker Diarization with Speaker-Wise Chain Rule
Yusuke Fujita, Shinji Watanabe, Shota Horiguchi +3
Speaker diarization is an essential step for processing multi-speaker audio. Although an end-to-end neural diarization (EEND) method achieved state-of-the-art performance, it is li…
Online End-to-End Neural Diarization with Speaker-Tracing Buffer
Yawen Xue, Shota Horiguchi, Yusuke Fujita +2
This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a speaker's…
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors
Shota Horiguchi, Yusuke Fujita, Shinji Watanabe +2
End-to-end speaker diarization for an unknown number of speakers is addressed in this paper. Recently proposed end-to-end speaker diarization outperformed conventional clustering-b…