Generation of Voice Signal Tone Sandhi and Melody Based on Convolutional Neural Network

Abstract

There is a need to prevent the generation of criminal activities in the voice signals due to changing voices by intruders to cover up their personal identities. The voice signal change detection based on convolutional neural network is proposed in this work that uses three commonly used voice processing software to change the tone of the voice library: Audacity, CoolEdit and RTISI. The research further raises 5 semitones for each voice, which are recorded at different levels, as +4, +5, +6, +7 and +8 respectively. Simultaneously, every speech is lowered by 5 halftones and which are further represented as -4, -5, -6, -7 and -8 respectively. The convolution neural network corresponding to network b-3 is determined as the final classifier in this article through experiments. The average accuracy A1 of its three categories has reached more than 97%, the detection accuracy A2 of electronic tone sandhi speech has reached more than 97%, and the false alarm rate FAR of the original speech is less than 1.9%. The outcomes obtained shows that the detection algorithm in this paper is effective, and it has good generalization ability.