Automatic speech segmentation for air-ground communication based on multi-input CGRU neural network

Abstract:

Automatic Speech segmentation is a very important preprocessing approach in many largescale applications such as speech recognition, speaker recognition and speech noise reduction. The performance of the segmentation algorithm directly affects the accuracy of the system output. In the air traffic control, the quality of the channel, the weather factor and the workload level of the speaker hugely affect the speech segmentation performance. In this paper, by analyzing the speech feature of airground communication, an automatic speech segmentation approach is proposed based on CGRU network. The proposed method analyzes the characteristics of airground communication, and uses the deep learning method to further extract the timedomain and frequencydomain nonlinear features of the speech signal, and classifies the speech signal frame into three categories: speech, end signal and others. The experiment compares the effects of multiple speech features as input on the segmentation effect, and verifies the performance of GMM, CNN, CLDNN, CGRU and other segmentation algorithms on the airground communication test dataset, a simple prediction result smoothing algorithm is presented. The experimental results show that the automatic segmentation method proposed in this paper has obvious advantages in airground communication, the AUC value of the classification model reaches 0.98.