• Corpus ID: 219179559

Neural Speaker Diarization with Speaker-Wise Chain Rule

@article{Fujita2020NeuralSD,
  title={Neural Speaker Diarization with Speaker-Wise Chain Rule},
  author={Yusuke Fujita and Shinji Watanabe and Shota Horiguchi and Yawen Xue and Jing Shi and Kenji Nagamatsu},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.01796}
}
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 limited to a fixed number of speakers. In this paper, we solve this fixed number of speaker issue by a novel speaker-wise conditional inference method based on the probabilistic chain rule. In the proposed method, each speaker's speech activity is regarded as a single random variable, and is estimated sequentially… 
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EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
TLDR
Experiments show that the proposed method outperforms the baselines in terms of diarization and separation performance for both fixed and flexible numbers of speakers, as well as speaker counting performance forfiic number of speakers.
End-To-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings
TLDR
An end-to-end deep network model that performs meeting diarization from single-channel audio recordings, designed to handle meetings with unknown numbers of speakers, using variable-number permutation-invariant cross-entropy based loss functions.
End-To-End Speaker Diarization as Post-Processing
TLDR
This paper proposes to use a two-speaker end-to-end diarization method as post-processing of the results obtained by a clustering-based method, and shows that the proposed algorithm consistently improved the performance of the state-of-the-art methods across CALLHOME, AMI, and DIHARD II datasets.
Online End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers
TLDR
An online end-to-end diarization that can handle overlapping speech and flexible numbers of speakers is proposed that achieves comparable performance to the offline EEND method and shows better performance on the DIHARD II dataset.
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection
TLDR
This paper proposes a novel multitask learning framework that solves speaker diarization and a desired subtask while explicitly considering the task dependency, and outperforms conventional EEND systems in terms of diarized error rate.
Robust End-to-end Speaker Diarization with Generic Neural Clustering
TLDR
Experimental show that when integrating an attractor-based chunk-level predictor, the proposed neural clustering approach can yield better Diarization Error Rate (DER) than the constrained K-means-based clustering approaches under the mismatched conditions.
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TLDR
This study extends the proposed conditional chain model to NAR multi-speaker ASR, and can even better than the PIT-Conformer AR model with only 1/7 latency, obtaining WERs of 19.9% and 34.3% on WSJ0-2mix and WSJ 0-3mix sets.
Dive: End-to-End Speech Diarization Via Iterative Speaker Embedding
TLDR
DIVE presents the diarization task as an iterative pro-cess: it repeatedly builds a representation for each speaker before predicting their voice activity conditioned on the ex-tracted representations, which intrinsically resolves the speaker ordering ambiguity without requiring the classi-cal permutation invariant training loss.
Robust End-to-End Speaker Diarization with Conformer and Additive Margin Penalty
TLDR
This work aims to improve the EEND-EDA model by increasing the robustness of the model by incorporating an additive margin penalty for minimizing the intra-class variance, and replacing the Transformer encoders with Conformerencoders to capture local information.
Separation Guided Speaker Diarization in Realistic Mismatched Conditions
TLDR
The proposed SGSD system can significantly improve the performance of state-of-the-art CSD systems, yielding relative diarization error rate reductions of 20.2 % and 20.8 % on the development set and evaluation set, respectively.
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