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Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

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Automating Speech Audiometry in Quiet and in Noise Using a Deep Neural Network.

Hadrien Jean¹, Nicolas Wallaert^1,2, Antoine Dreumont³

¹R&D Department, My Medical Assistant SAS, 51100 Reims, France.

|February 26, 2025

Summary

An automated speech recognition (ASR) system accurately scores speech understanding in quiet and noise, matching human experts. This deep neural network offers a reliable, efficient tool for hearing evaluations in clinical and research settings.

Keywords:

automated speech recognition deep neural network machine learning speech audiometry speech-in-noise

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Area of Science:

Audiology
Speech Science
Artificial Intelligence in Healthcare

Background:

Comprehensive hearing evaluations include speech understanding assessment.
Current speech audiometry requires time-consuming manual scoring by professionals.
Automated scoring methods are needed to improve efficiency.

Purpose of the Study:

To develop and validate an automated speech recognition (ASR) system for phonetic-level scoring of speech audiometry.
To assess the performance and reliability of the ASR system in clinical settings.
To compare the ASR system's scoring accuracy and test-retest reliability against manual scoring.

Main Methods:

Developed a deep neural network-based automated speech recognition (ASR) system.
Trained the ASR system using French speech materials (Lafon's cochlear lists, Dodelé logatoms).
Evaluated the ASR system's performance and reliability with normal-hearing and hearing-impaired listeners in quiet and noisy conditions.

Main Results:

The ASR system demonstrated statistically similar performance to manual scoring by expert hearing professionals.
Automated scoring accuracy was consistent in both quiet and noisy listening conditions.
The test-retest reliability of the automated scoring closely matched that of manual scoring.

Conclusions:

The developed deep neural network-based ASR system provides an accurate and reliable method for speech audiometry.
This automated system is validated for use in both clinical practice and research for hearing evaluations.
The ASR system offers an efficient alternative to manual scoring, improving the assessment of speech understanding.