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Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Updated: Sep 28, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants.

Lionel Fontan1, Libio Gonçalves Braz2, Julien Pinquier2

  • 1Archean LABS, Montauban, France.

Frontiers in Neuroscience
|April 4, 2022
PubMed
Summary
This summary is machine-generated.

Optimizing hearing aid (HA) time constants significantly improved speech recognition for age-related hearing loss when using standard gains. However, optimization did not benefit ASR-predicted performance with advanced, ASR-derived gains.

Keywords:
age-related hearing lossage-related hearing loss (ARHL)attack timeautomatic speech recognitionautomatic speech recognition (ASR)compression speedhearing aidshearing aids (HAs)random searchrandom search (RS)release time

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Area of Science:

  • Audiology
  • Speech Processing
  • Computational Auditory Perception

Background:

  • Automatic speech recognition (ASR) combined with hearing loss (HL) and hearing aid (HA) simulations can predict speech identification performance in individuals with age-related hearing loss.
  • ASR facilitates the evaluation and optimization of HA configurations, including insertion gains and compression thresholds, for personalized HA fitting.

Purpose of the Study:

  • To investigate the efficacy of a random-search algorithm in optimizing hearing aid time constants (attack and release times) for 12 distinct audiometric profiles.
  • To compare the impact of optimizing time constants using standard CAM2 insertion gains versus ASR-optimized gains, with ASR-optimized compression thresholds.

Main Methods:

  • A hearing aid simulator and a hearing loss simulator were employed to process speech stimuli based on audiometric profiles.
  • A random-search algorithm was utilized to iteratively adjust time constants, aiming to maximize ASR performance for each audiometric profile over 1,000 iterations.
  • The random search was performed twice to evaluate the reproducibility of the optimized time-constant configurations and resulting ASR scores.

Main Results:

  • Optimizing time constants led to a significant improvement in ASR scores when using CAM2 insertion gains.
  • No significant improvement in ASR scores was observed when optimizing time constants with ASR-based insertion gains.
  • While repeating the random search produced similar ASR scores, the specific time-constant configurations varied.

Conclusions:

  • Time constant optimization is beneficial for improving ASR-predicted speech recognition with conventional HA gain prescriptions.
  • The benefits of time constant optimization may be limited when using advanced, ASR-derived gain strategies.
  • Further research is needed to understand the interaction between different HA parameters and their impact on speech recognition outcomes.