Speech Intelligibility Prediction for Hearing Aids Using an Auditory Model and Acoustic Parameters

Objective speech intelligibility (SI) metrics for hearing-impaired people play an important role in hearing aid development. The work on improving SI prediction also became the basis of the first Clarity Prediction Challenge (CPC1). This study investigates a physiological auditory model called EarModel and acoustic parameters for SI prediction. EarModel is utilized because it provides advantages in estimating human hearing, both normal and impaired. The hearing-impaired condition is simulated in EarModel based on audiograms; thus, the SI perceived by hearing-impaired people is more accurately predicted. Moreover, the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) and WavLM, as additional acoustic parameters for estimating the difficulty levels of given utterances, are included to achieve improved prediction accuracy. The proposed method is evaluated on the CPC1 database. The results show that the proposed method improves the SI prediction effects of the baseline and hearing aid speech prediction index (HASPI). Additionally, an ablation test shows that incorporating the eGeMAPS and WavLM can significantly contribute to the prediction model by increasing the Pearson correlation coefficient by more than 15% and decreasing the root-mean-square error (RMSE) by more than 10.00 in both closed-set and open-set tracks.