Abstract: Aiming at the fact that there is no breakthrough in modeling for the electronic disguised voicer estoration,this paper proposed a new model based on Dilated Casual-Convolution Neural Network (DC-CNN) for restoring electronic disguised voice.DC-CNN is used as the framework of restoring model,and convolution and nonlinear mapping are performed on the historical sampling acoustic information and restoring factors of the electronic disguised voice.Meanwhile,the model’s neural network adopts skip-connection for deep transmission and outputs the restoring voice after companding transformation.The model has obvious characteristics such as nonlinear mapping,expansibility,adaptability and conditionality,concurrency,etc.In the experiment,the original voice was processed by three basic disguised functions:pitch,tempo and rate.Then,voiceprint features comparison,LPC analysis and voice identity of human audiometry recognition were made between restoring voice and original voice.The voiceprint of the restoringvoice fits that of the original voice perfectly,and high quality formant waveform restoration is achieved.The piano music’s and English voice’sgeneral restoring fitting rates of the formant’s parameters are 79.03% and 79.06% respectively,which are much higher than the similarity of electronic disguised voice to original voice.The results turn out that this model can minify the electronic disguised characteristics effectively and it is efficient on the restoration of electronic disguised piano music and English voice

Key words: DC-CNN, Electronic disguised voice, Gated activation units, Restoring voice, Restoring factor