A Mandarin Speech Intelligibility Prediction Model for Hearing Aids

Objective speech intelligibility prediction methods provide a low-cost alternative to tests with human listeners, allowing the investigation of many factors affecting intelligibility. However, the objective prediction of speech intelligibility for Mandarin-speaking listeners with hearing impairment is largely underdeveloped. Existing intelligibility metrics are based on English linguistic structures and/or are designed primarily for normal-hearing listeners. This paper introduces a Mandarin Chinese Hearing-Aid Speech Perception Index (MCHASPI), an objective metric for predicting the intelligibility of speech processed by hearing aids for Mandarin Chinese speakers with or without hearing loss. A key challenge was the scarcity of high-quality datasets and the lack of models tailored to the perceptual characteristics of Mandarin Chinese. To address this, we first constructed a Mandarin intelligibility dataset using subjective hearing tests, for speech with six types of distortions. A hearing loss simulator was employed to preprocess the reference (undistorted) and test speech signals. Multiple features were extracted from these processed signals and combined for subsequent analysis. Two prediction models were developed, namely MCHASPI-NN and MCHASPI-L, utilizing a neural network and a logistic mapping function, respectively, to predict intelligibility from feature values. The core of the MCHASPI model involves a perceptually motivated segmental stretching scheme for precise signal alignment, followed by frame-wise threshold detection and multi-feature fusion. Evaluations with normal-hearing and hearing-impaired listeners demonstrated that MCHASPI gave significantly more accurate predictions than existing models across all tested datasets. This validates the effectiveness of MCHASPI for assessing Mandarin speech intelligibility for populations with varying degrees of hearing loss.