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Related Experiment Video

Updated: Sep 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Compressing speaker extraction model with ultra-low precision quantization and knowledge distillation.

Yating Huang1, Yunzhe Hao1, Jiaming Xu2

  • 1Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 16, 2022
PubMed
Summary
This summary is machine-generated.

We developed TinyWASE, a lightweight speaker extraction model for resource-constrained devices. This model achieves significant compression (8.97x) with minimal performance loss, enabling efficient on-device audio processing.

Keywords:
Knowledge distillationParameter sharingQuantization-aware trainingSpeaker extraction

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Area of Science:

  • Speech processing
  • Machine learning
  • Model compression

Background:

  • Speaker extraction models like WASE offer high performance but are too large for resource-constrained devices.
  • Deploying advanced AI models on edge devices requires significant reductions in model size and computational cost.

Purpose of the Study:

  • To develop a lightweight speaker extraction model (TinyWASE) suitable for resource-constrained devices.
  • To investigate the effectiveness of quantization-aware training and knowledge distillation for speaker extraction.

Main Methods:

  • Proposed TinyWASE, a compressed version of the WASE speaker extraction model.
  • Utilized Distillation-aware Quantization, combining quantization-aware training and knowledge distillation.
  • Evaluated performance on the WSJ0-2mix dataset.

Main Results:

  • TinyWASE achieved comparable performance to the full-precision model with an 8.97x compression ratio and a 2.15 MB model size using 3-bit quantization.
  • Combined with parameter sharing, TinyWASE reached a 23.81x compression ratio with limited performance degradation.

Conclusions:

  • TinyWASE effectively enables high-performance speaker extraction on resource-constrained devices.
  • Model compression techniques, particularly Distillation-aware Quantization, are crucial for deploying advanced speech models in edge computing scenarios.