A machine learning approach to fitting prescription for hearing aids

Abstract

A successful Hearing-Aid Fitting (HAF) is more than just selecting an appropriate Hearing Aid (HA) device for a patient with Hearing Loss (HL). The initial fitting is given by the prescription based on user’s hearing loss; however, it is often necessary for the audiologist to readjust some parameters to satisfy the user demands. Therefore, in this paper, we concentrated on a new application of Neural Network (NN) combined with a Transfer Learning (TL) strategy to develop a fitting algorithm with the prescription database for hearing loss and readjusted gain to minimize the gap between fitting satisfaction. As prior information, we generated the data set from two popular hearing-aid fitting software, then fed the training data to our proposed model, and verified the performance of the architecture. Pondering real life circumstances, where numerous fitting records may not always be accessible, we first investigated the number of minimum fitting records required for possible sufficient training. After that, we evaluated the performance of the proposed algorithm in two phases: (a) NN with refined hyper parameter showed enhanced performance in compare to state-of-the-art DNN approach, and (b) the TL approach boosted the performance of the NN algorithm in a broad way. Altogether, our model provides a pragmatic and promising tool for HAF.