27 citations · 30 across the 6 of their papers we have counts for
6 papers
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization
Shota Horiguchi, Yuki Takashima, Shinji Watanabe +1
Due to the high performance of multi-channel speech processing, we can use the outputs from a multi-channel model as teacher labels when training a single-channel model with knowle…
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…
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
Yuki Takashima, Yusuke Fujita, Shota Horiguchi +3
In this paper, we present a semi-supervised training technique using pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has shown promising performance compa…
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection
Yuki Takashima, Yusuke Fujita, Shinji Watanabe +3
In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND). The EEND system has shown promising performance compared with tr…
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…