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Related Experiment Video

Updated: May 2, 2026

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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A dual-region speech enhancement method based on voiceprint segmentation.

Yang Li1, Wei-Tao Zhang1, Shun-Tian Lou1

  • 1School of Electronic Engineering, Xidian University, Xi'an 710071, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 10, 2024
PubMed
Summary

This study introduces a novel dual-region speech enhancement model. By segmenting speech into distinct regions, the model improves the mapping of noisy to clean speech, significantly boosting performance.

Keywords:
Deep learningRegression taskSpeech enhancementVoiceprint segmentation

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Area of Science:

  • Speech processing
  • Deep learning
  • Signal processing

Background:

  • Single-channel speech enhancement commonly uses deep learning to separate clean speech from noise.
  • Current models map noisy to clean speech, but this is complex due to varying speech energy distribution.
  • A single model struggles with distinct mapping requirements in high and low speech energy regions.

Purpose of the Study:

  • To propose a dual-region speech enhancement model to address the limitations of single-model approaches.
  • To improve speech enhancement by treating regions with concentrated and non-concentrated speech energy separately.
  • To enhance the performance and reduce the complexity of speech enhancement models.

Main Methods:

  • A voiceprint segmentation model was developed to classify noisy speech into two distinct regions.
  • Separate deep learning models were trained for each identified speech region.
  • The outputs from the dual-region models were merged to reconstruct the enhanced speech signal.

Main Results:

  • The proposed dual-region model achieved competitive speech enhancement performance on public datasets.
  • The method outperformed existing state-of-the-art speech enhancement techniques.
  • Ablation studies confirmed the effectiveness of the dual-region approach in improving model performance.

Conclusions:

  • The dual-region speech enhancement model effectively handles the distinct characteristics of speech energy distribution.
  • Separating the mapping tasks for different speech regions enhances overall enhancement performance.
  • This approach offers a more effective strategy for single-channel speech enhancement compared to traditional methods.