Advancing Hearing Assessment: An ASR-Based Frequency-Specific Speech Test for Diagnosing Presbycusis

TL;DR

This paper introduces a novel ASR-based frequency-specific speech test that simulates and analyzes phoneme confusions caused by presbycusis, aiming to improve detailed hearing loss diagnostics beyond traditional audiometry.

Contribution

It develops a new ASR-driven method to simulate and analyze phoneme-level effects of hearing loss, providing a more granular and objective diagnostic tool for presbycusis.

Findings

Simulated hearing loss causes specific phoneme confusions, especially in high-frequency consonants.

The test battery effectively differentiates between normal and impaired hearing in simulations.

Phoneme deletions increase with simulated hearing impairment.

Abstract

Traditional audiometry often fails to fully characterize the functional impact of hearing loss on speech understanding, particularly supra-threshold deficits and frequency-specific perception challenges in conditions like presbycusis. This paper presents the development and simulated evaluation of a novel Automatic Speech Recognition (ASR)-based frequency-specific speech test designed to provide granular diagnostic insights. Our approach leverages ASR to simulate the perceptual effects of moderate sloping hearing loss by processing speech stimuli under controlled acoustic degradation and subsequently analyzing phoneme-level confusion patterns. Key findings indicate that simulated hearing loss introduces specific phoneme confusions, predominantly affecting high-frequency consonants (e.g., alveolar/palatal to labiodental substitutions) and leading to significant phoneme deletions, consistent with the acoustic cues degraded in presbycusis. A test battery curated from these ASR-derived confusions demonstrated diagnostic value, effectively differentiating between simulated normal-hearing and hearing-impaired listeners in a comprehensive simulation. This ASR-driven methodology offers a promising avenue for developing objective, granular, and frequency-specific hearing assessment tools that complement traditional audiometry. Future work will focus on validating these findings with human participants and exploring the integration of advanced AI models for enhanced diagnostic precision.