61 citations · 215 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
Shota Horiguchi, Yusuke Fujita, Shinji Watanabe +2
This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization,…
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…
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…
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
Yusuke Fujita, Shinji Watanabe, Shota Horiguchi +2
The most common approach to speaker diarization is clustering of speaker embeddings. However, the clustering-based approach has a number of problems; i.e., (i) it is not optimized…
End-to-End Neural Speaker Diarization with Self-attention
Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi +3
Speaker diarization has been mainly developed based on the clustering of speaker embeddings. However, the clustering-based approach has two major problems; i.e., (i) it is not opti…