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Improving Label Assignments Learning by Dynamic Sample Dropout Combined with Layer-wise Optimization in Speech

Chenyang Gao1, Yue Gu2, Ivan Marsic1

  • 1Rutgers University.

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Summary
This summary is machine-generated.

Dynamic sample dropout and layer-wise optimization improve supervised speech separation by reducing label assignment switching and layer decoupling. This novel approach enhances model learning for better speech separation performance.

Keywords:
Speech separationdynamic sample dropoutlayer-wise optimizationpermutation invariant training

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Permutation Invariant Training (PIT) is standard for supervised speech separation, addressing label ambiguity.
  • Excessive label assignment switching in PIT hinders effective model learning.
  • Layer-decoupling issues also impact speech separation performance.

Purpose of the Study:

  • To introduce a novel training strategy to mitigate label assignment switching in speech separation.
  • To improve speech separation performance by addressing layer-decoupling.
  • To enhance the learning of optimal label assignments in supervised speech separation models.

Main Methods:

  • Dynamic Sample Dropout (DSD): Excludes samples negatively impacting label assignments based on prior performance.
  • Layer-Wise Optimization (LO): Addresses layer-decoupling issues for improved performance.
  • Combined DSD and LO strategy implemented for supervised speech separation.

Main Results:

  • The combined DSD and LO approach significantly outperforms baseline methods.
  • The proposed strategy effectively resolves excessive label assignment switching.
  • Layer-decoupling issues are successfully addressed, leading to better performance.

Conclusions:

  • The DSD and LO strategy offers an effective solution for supervised speech separation challenges.
  • This approach is easy to implement, requires no additional data or training steps, and is versatile across different speech separation tasks.