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Related Experiment Video

Updated: Jul 10, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Speech Perception Improvement Algorithm Based on a Dual-Path Long Short-Term Memory Network.

Hyeong Il Koh1, Sungdae Na2, Myoung Nam Kim3

  • 1Department of Medical & Biological Engineering, Graduate School, Kyungpook National University, Daegu 41944, Republic of Korea.

Bioengineering (Basel, Switzerland)
|November 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dual-path deep learning model for speech enhancement. The model improves speech quality and intelligibility by processing both time and time-frequency domains simultaneously.

Keywords:
LSTMSTFTdual-path networkencoder–decoder structuremel-filter banksmerge algorithmspectral extension blockspeech enhancement

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Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Acoustics

Background:

  • Deep learning speech enhancement often focuses on time-frequency representations.
  • Conventional methods can cause speech distortion due to domain-specific processing limitations.

Purpose of the Study:

  • To develop a novel speech enhancement model addressing limitations of single-domain approaches.
  • To improve speech quality and intelligibility by integrating temporal and spectral feature extraction.

Main Methods:

  • A dual-path deep learning network was designed, incorporating both time and time-frequency domains.
  • The time path models semantic features from the waveform, while the time-frequency path refines spectral details.
  • Long Short-Term Memory (LSTM) networks were utilized to model mask functions for enhancing temporal and spectral features.

Main Results:

  • The proposed dual-path LSTM network demonstrated superior performance compared to single-domain methods.
  • Consistent improvements were observed in both objective and subjective evaluations of speech quality.
  • Enhanced intelligibility was a key outcome of the dual-path approach.

Conclusions:

  • The dual-path architecture effectively captures complementary speech information from different domains.
  • This model offers a more comprehensive and robust solution for speech enhancement challenges.
  • The findings suggest a promising direction for future deep learning-based audio processing research.