A Non-Intrusive speech quality evaluation algorithm for hearing aids via an auxiliary training task

Abstract

With the increasing severity of hearing problems caused by an aging population, it has become more urgent to study speech quality evaluation methods for hearing aids to automatically judge the effect of parameter adjustments to solve the problem of lack of audiologists. A new evaluation algorithm, which is based on the multi-task learning strategy that combines the main task of non-intrusive speech quality evaluation with the auxiliary task of quality classification, is proposed. The main task network is composed of a bi-directional long short-term memory (BiLSTM) network combined with the self-attention mechanism, and the auxiliary task network is formed by fully connected layers based on batch normalization. The proposed algorithm firstly uses a convolutional neural network (CNN) to extract frame-level features. Then, the features are inputted into the main task network and the auxiliary task network to extract utterance-level features, respectively. Finally, the objective scores and quality ratings are obtained by the different mappers. Evaluation experiments of the Hearing Aid Speech Quality Index (HASQI) show that compared with others algorithms, the proposed algorithm effectively improves the speech quality evaluation. And it also shows strong robustness and good generalization performances under different distorted conditions.