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Related Experiment Video

Updated: Nov 5, 2025

Decoding Natural Behavior from Neuroethological Embedding
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Combination of deep speaker embeddings for diarisation.

Guangzhi Sun1, Chao Zhang1, Philip C Woodland1

  • 1Cambridge University Engineering Department, Trumpington Street, Cambridge, CB2 1PZ, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|May 13, 2021
PubMed
Summary

This study introduces c-vectors, a novel method for speaker embeddings, significantly improving speaker diarisation accuracy. The new approach enhances performance on challenging datasets, demonstrating greater robustness in real-world conditions.

Keywords:
Attention mechanismBilinear poolingGating mechanismSpeaker diarisationSpeaker embeddingSystem combination

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Area of Science:

  • Speech processing
  • Machine learning
  • Artificial intelligence

Background:

  • Speaker diarisation has advanced with neural network (NN) derived d-vectors for speaker embeddings.
  • Existing d-vectors face limitations in performance and robustness for clustering speech segments.

Purpose of the Study:

  • To propose a novel c-vector method for extracting more robust and higher-performing speaker embeddings.
  • To develop a unified neural-based single-pass speaker diarisation pipeline.

Main Methods:

  • Combining complementary d-vectors using 2D self-attentive, gated additive, and bilinear pooling structures.
  • Implementing a neural network pipeline for voice activity detection, speaker change point detection, and embedding extraction.
  • Conducting experiments on the AMI and NIST RT05 datasets.

Main Results:

  • Relative speaker error rate (SER) reductions of 13% and 29% on AMI dev and eval sets using c-vectors over d-vectors.
  • A 15% relative SER reduction on the RT05 dataset, demonstrating robustness.
  • Further improvements with the best c-vector system achieving 7-17% relative SER reduction when incorporating VoxCeleb data.

Conclusions:

  • The proposed c-vector method offers significant improvements in speaker diarisation accuracy and robustness compared to d-vectors.
  • The neural-based single-pass pipeline effectively integrates multiple diarisation tasks.
  • The findings highlight the potential of advanced embedding techniques for complex acoustic environments.