Acoustic analysis and recognition feature algorithm of synthetic speech
With the frequent occurrence of telecommunication fraud cases in the current new social crimes,a method that can automatically and effectively distinguish the authenticity of speech is urgently needed.To further enhance the current capability of detecting synthetic speech in the field of deep learning and to provide technical support for securing speech information,we analyze and compare the acoustic characteristics of synthetic speech and real speech,design an acoustic feature root mean square angle to quantify the variation of speech intensity,combine fundamental frequency variation and speech narrowband spectrogram acoustic features for fusion,quantify the difference of acoustic characteristics,and focus on the key acoustic information in synthetic speech.The fusion of input acoustic features in the neural network model yielded an equal error rate of 0.6％on the validation set of the FoR dataset,and the best result reached an equal error rate of 10.8％on the test set.The recognition of synthetic speech was successfully achieved,confirming the effectiveness of acoustic features and the feasibility of the research scheme of this paper,broadening the research ideas of synthetic speech feature design to a certain extent.
NETL
NSTL
万方数据
维普
