Non-intrusive speech intelligibility prediction using an auditory periphery model with hearing loss

Abstract

Speech intelligibility prediction methods are necessary for hearing aid development. However, many such prediction methods are categorized as intrusive metrics because they require reference speech as input, which is often unavailable in real-world situations. Additionally, the processing techniques in hearing aids may cause temporal or frequency shifts, which degrade the accuracy of intrusive speech intelligibility metrics. This paper proposes a non-intrusive auditory model for predicting speech intelligibility under hearing loss conditions. The proposed method requires binaural signals from hearing aids and audiograms representing the hearing conditions of hearing-impaired listeners. It also includes additional acoustic features to improve the method's robustness in noisy and reverberant environments. A two-dimensional convolutional neural network with neural decision forests is used to construct a speech intelligibility prediction model. An evaluation conducted with the first Clarity Prediction Challenge dataset shows that the proposed method performs better than the baseline system.