Interference Quality Assessment of Speech Communication Based on Deep Learning

Abstract

In this article, interferencequality assessment is of great significance to reflect the communication environment and improve speech communication performance. However, most traditional assessment approaches aimed at the degraded speech produced in the communication telephone network, but lacking of methods in extreme communication environment with ultralow SNR. Therefore, in this article, we proposed a convolutional neural network (CNN) model evaluation method based on Log-Mel spectrogram to evaluate interfered speech quality. In this method, the Mel frequency cepstrum coefficients of interference speech are converted into images, which are used as input of CNN. In order to verify the performance of this method, we collected a speech dataset in real interfered communication scenarios and finished manual annotation. Experiments are carried out on this dataset to evaluate the interference speech, and the performance of this method is compared with that of machine learning evaluation method under different features. Experimental results show that the proposed method gives the better evaluation accuracy. Compared with the previous machine learning methods, the accuracy is improved by 12.5% from 75% to 87.5%.