27 citations · 55 across the 9 of their papers we have counts for
9 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…
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models
Yuki Takashima, Shota Horiguchi, Shinji Watanabe +2
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model. Conventional app…
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
Shota Horiguchi, Shinji Watanabe, Paola Garcia +2
A method to perform offline and online speaker diarization for an unlimited number of speakers is described in this paper. End-to-end neural diarization (EEND) has achieved overlap…
Multi-Channel End-to-End Neural Diarization with Distributed Microphones
Shota Horiguchi, Yuki Takashima, Paola Garcia +2
Recent progress on end-to-end neural diarization (EEND) has enabled overlap-aware speaker diarization with a single neural network. This paper proposes to enhance EEND by using mul…
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…