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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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OPRA-RS: A Hearing-Aid Fitting Method Based on Automatic Speech Recognition and Random Search.

Libio Gonçalves Braz1, Lionel Fontan2, Julien Pinquier1

  • 1IRIT, CNRS, Université Paul Sabatier, Toulouse, France.

Frontiers in Neuroscience
|March 10, 2022
PubMed
Summary

This study introduces a new hearing aid fitting method, OPRA-RS, which uses automatic speech recognition (ASR) and random search (RS) to optimize settings. OPRA-RS significantly improved speech intelligibility predictions compared to traditional methods.

Keywords:
age-related hearing loss (ARHL)automatic speech recognition (ASR)compression thresholdshearing aids (HAs)insertion gainsprescription rulerandom search (RS)

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Area of Science:

  • Audiology
  • Artificial Intelligence
  • Signal Processing

Background:

  • Hearing aid (HA) prescription rules (e.g., NAL-NL2, DSL-v5, CAM2) guide initial HA settings.
  • Current fine-tuning relies on time-consuming speech intelligibility tests, potentially leading to suboptimal patient fittings.
  • Automatic speech recognition (ASR) shows promise in predicting the impact of insertion gains (IGs) on speech intelligibility for age-related hearing loss (ARHL).

Purpose of the Study:

  • To extend ASR applications by optimizing both IGs and compression thresholds (CTs) for hearing aids.
  • To develop an objective hearing aid fitting method combining ASR with random-search (RS) genetic algorithms.
  • To compare the performance of the new method (OPRA-RS) against a standard prescription rule (CAM2).

Main Methods:

  • Developed an objective prescription rule based on ASR and random search (OPRA-RS).
  • Utilized three RS genetic algorithms to limit the number of configurations assessed for optimizing IGs and CTs.
  • Computed optimal HA settings for 12 audiograms representing various ARHL severities and compared ASR performance with CAM2 settings.

Main Results:

  • OPRA-RS achieved significantly higher ASR scores compared to the CAM2 rule across all RS algorithms.
  • RS algorithms demonstrated reliability, converging on similar optimal HA settings across repetitions.
  • Differences were noted between RS algorithms regarding maximum ASR performance and computational cost.

Conclusions:

  • The OPRA-RS method, combining ASR and RS, offers a promising approach for hearing aid fine-tuning.
  • This objective method has the potential to enhance speech intelligibility for patients with hearing loss.
  • Further research can explore the clinical benefits and refine the application of ASR and RS in audiology.