Deep Neural Network Based Noised Asian Speech Enhancement and Its Implementation on a Hearing Aid App

Abstract

This article studies noised Asian speech enhancement based on the deep neural network (DNN) and its implementation on an app. We use the THCHS-30 speech dataset and the common noise dataset in daily life as training and testing data of the DNN. To stack the frequency data of multiple audio frames to improve the effect of speech enhancement, the system compares the best number of stacked frames during training and testing. At the same time, the influence of training rounds on the PESQ is compared, and the best number of rounds is obtained. On this basis, the best model is implemented on the hearing aid app, and the real-time performance of the device is tested. The experiment shows that based on the DNN, using an appropriate number of rounds for training and using an appropriate number of audio frames stacking to improve the speech enhancement effect, and transplanting this speech enhancement model to the hearing aid app, can effectively improve speech clarity and intelligibility within a reasonable time delay range.