Research on Modeling of Vocal State Duration Based on Spectrogram Analysis

Abstract

In the early stage of vocal music education, students generally do not understand the structure of the human body, and have doubts about how to pronounce their voices scientifically. However, with the continuous development of computers, computer technology has become more and more developed, and computer processing speed has been greatly increased, which provides favorable conditions for the development of the application of vocal spectrum analysis technology in vocal music teaching. In this paper, we first study the GMM-SVM and DBN, and combine them to extract the deep Gaussian super vector DGS, and further construct the feature DGCS on the basis of DGS; then we study the convolutional neural network (CNN), which has achieved great success in the image recognition task in recent years, and design a CNN model to extract the deep fusion features of vocal music. The experimental simulations show that the CNN fusion-based speaker recognition system achieves very good results in terms of recognition rate.