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Related Experiment Video

Updated: Jun 1, 2025

Author Spotlight: Optimizing EAS with Long Electrodes for Enhanced Cochlear Coverage and Hearing Preservation
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Author Spotlight: Optimizing EAS with Long Electrodes for Enhanced Cochlear Coverage and Hearing Preservation

Published on: October 11, 2024

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Speech Enhancement for Cochlear Implant Recipients using Deep Complex Convolution Transformer with Frequency

Nursadul Mamun1, John H L Hansen1

  • 1CRSS: Center for Robust Speech Systems; Cochlear Implant Processing Laboratory (CILab), Department of Electrical and Computer Engineering, University of Texas at Dallas, USA.

IEEE/ACM Transactions on Audio, Speech, and Language Processing
|January 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Deep Complex Convolution Transformer Network (DCCTN) to improve speech understanding for cochlear implant (CI) users by enhancing both speech magnitude and phase. The new method significantly boosts intelligibility in noisy environments.

Keywords:
Complex-valued NetworkDeep Neural NetworkFrequency Transformation BlockSpeech EnhancementTransformerU-Net

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Auditory Neuroscience

Background:

  • Cochlear implant (CI) users face communication challenges in noisy environments due to distorted speech signals.
  • Existing speech enhancement (SE) methods often neglect the crucial role of phase information in speech perception.

Purpose of the Study:

  • To develop a novel deep learning model for simultaneous enhancement of speech magnitude and phase spectra.
  • To improve speech intelligibility and quality for CI users in complex acoustic environments.

Main Methods:

  • A Deep Complex Convolution Transformer Network (DCCTN) was proposed, utilizing a complex-valued U-Net with a transformer in the bottleneck.
  • The network incorporates a frequency transformation block to capture speech harmonic correlations.
  • DCCTN learns a complex transformation matrix for time-frequency domain speech recovery.

Main Results:

  • DCCTN outperformed existing SE models (CRN, DCCRN, GCRN) in objective speech intelligibility and quality metrics.
  • Formal listener evaluations with CI recipients confirmed significant improvements in noisy conditions.
  • The model effectively suppressed non-stationary noise without introducing musical artifacts.

Conclusions:

  • The proposed DCCTN offers a superior approach to speech enhancement for CI users by addressing both magnitude and phase distortions.
  • This method holds promise for enhancing real-world communication for individuals with hearing impairments.