Corpus ID: 204824120

Discriminative Neural Clustering for Speaker Diarisation

@article{Li2019DiscriminativeNC,
  title={Discriminative Neural Clustering for Speaker Diarisation},
  author={Qiujia Li and Florian L. Kreyssig and Chao Zhang and Philip C. Woodland},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.09703}
}
  • Qiujia Li, Florian L. Kreyssig, +1 author Philip C. Woodland
  • Published in ArXiv 2019
  • Computer Science, Engineering
  • This paper proposes a novel method for supervised data clustering. The clustering procedure is modelled by a discriminative sequence-to-sequence neural network that learns from examples. The effectiveness of the Transformer-based Discriminative Neural Clustering (DNC) model is validated on a speaker diarisation task using the challenging AMI data set, where audio segments need to be clustered into an unknown number of speakers. The AMI corpus contains only 147 meetings as training examples for… CONTINUE READING

    Citations

    Publications citing this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES

    Fully Supervised Speaker Diarization

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    End-to-End Neural Speaker Diarization with Self-Attention

    VIEW 1 EXCERPT

    PyHTK: Python Library and ASR Pipelines for HTK

    VIEW 2 EXCERPTS

    Speaker Diarisation Using 2D Self-attentive Combination of Embeddings

    VIEW 2 EXCERPTS

    ESPnet: End-to-End Speech Processing Toolkit

    VIEW 2 EXCERPTS