61 citations · 215 across the 10 of their papers we have counts for
10 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…
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…
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…
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…
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models
Naoyuki Kanda, Shota Horiguchi, Yusuke Fujita +3
This paper investigates the use of target-speaker automatic speech recognition (TS-ASR) for simultaneous speech recognition and speaker diarization of single-channel dialogue recor…
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…