Development of a Method to Assess In-Ear Speech Intelligibility Through Listening Effort

Résumé

Active hearing protection devices equipped with an in-ear microphone enable in-ear voice pickup which, presents better signal-to-noise ratio over ambient microphones in highly noisy conditions. To improve its intelligibility, in-ear speech requires processing which can take many forms, from fixed filtering to spectral domain processing based on machine learning. Comparing these processing strategies objectively can be difficult. In the authors’ experience, objective intelligibility assessment techniques like the modified rhyme test have not provided the necessary resolution to compare various processed in-ear speech. They have also failed to capture a concept of listening effort, which intuitively seemed to increase with in-ear speech over reference speech whenrecorded in silence.This work presents the early development and preliminary validation of a technique that objectively measures intelligibility of speech material and its associated listening effort. Using a dual task paradigm, the attentional and cognitive resources required to understand speech were quantified. Ten participants performed a closed-set word recognition task and visual pattern recognition task separately and concurrently. Accuracy data were collected for in-ear and reference speech in noise. Dual-task costs (DTC) were calculated.Speech intelligibility was low and listening effort was high for reference speech presented with an 85 dB(A) competing noise. When reducing the competing noise level to 80 dB(A), speech intelligibility improved, but listening effort remained important. While in-ear speech (with and without speech processing) presented with high levels of intelligibility, they also presented with better listening effort than reference speech with competing noise. Our results suggest that a dual-task paradigm to measure listening effort can be a good approach to behaviorally evaluate in-ear speech. Our pilot study supports the use of in-ear speech to improve communication in noisy settings while quantifying its potential for further improvements.