Feasibility of a hearing-aid speech quality index designed for noisy measurement conditions

Abstract

A goal in fitting hearing aids is to find settings that improve listener judgments of the amplified speech quality. Objective metrics that predict speech quality could assist in this fitting procedure and, thus, become useful tools in the clinic. The Hearing-Aid Speech Quality Index version 2 (HASQI v2) is effective in predicting speech quality ratings for speech processed through a hearing aid. However, HASQI v2 requires a signal-to-noise ratio (SNR) of approximately 40 dB to provide reliable measurements of low levels of nonlinear distortion, and this favorable an SNR is not always found in a clinical examination room or medical office where hearing-aid measurements are performed. Two modifications to HASQI v2 are considered in this presentation to improve the measurement accuracy in noisy situations. The first is applying dynamic-range compression to the test signal to enhance low-intensity speech sounds that could be masked by background noise, and the second is using a multilayer neural network to match noisy measurements to the corresponding measurements made in a quiet laboratory. The benefits of these modifications to HASQI are evaluated using a hearing-aid simulation, and verification is provided using commercial hearing-aid measurements made in an audiology clinic.