The problem of disguised voice recognition based on deep belief networks is studied. A hybrid feature extraction algorithm based on formants, Gammatone frequency cepstrum coefficients(GFCC) and their different coeffic...The problem of disguised voice recognition based on deep belief networks is studied. A hybrid feature extraction algorithm based on formants, Gammatone frequency cepstrum coefficients(GFCC) and their different coefficients is proposed to extract more discriminative speaker features from the original voice data. Using mixed features as the input of the model, a masquerade voice library is constructed. A masquerade voice recognition model based on a depth belief network is proposed. A dropout strategy is introduced to prevent overfitting, which effectively solves the problems of traditional Gaussian mixture models, such as insufficient modeling ability and low discrimination. Experimental results show that the proposed disguised voice recognition method can better fit the feature distribution, and significantly improve the classification effect and recognition rate.展开更多
基金Supported by the National Natural Science Foundation of China(21373245,21173242)the State Key Development Program for Basic Research of China(2013CB632404)+1 种基金the National High Technology Research and Development Program of China(2012AA051501)the Project Support of Gansu Provincial Science &Technology Department(1304FKCA085)
基金supported by Natural Science Foundation of Liaoning Province (Nos. 2019-ZD-0168 and 2020-KF-12-11)Major Training Program of Criminal Investigation Police University of China (No. 3242019010)+1 种基金Key Research and Development Projects of Ministry of Science and Technology (No. 2017YFC0821005)Second Batch of New Engineering Research and Practice Projects(No. E-AQGABQ20202710)。
文摘The problem of disguised voice recognition based on deep belief networks is studied. A hybrid feature extraction algorithm based on formants, Gammatone frequency cepstrum coefficients(GFCC) and their different coefficients is proposed to extract more discriminative speaker features from the original voice data. Using mixed features as the input of the model, a masquerade voice library is constructed. A masquerade voice recognition model based on a depth belief network is proposed. A dropout strategy is introduced to prevent overfitting, which effectively solves the problems of traditional Gaussian mixture models, such as insufficient modeling ability and low discrimination. Experimental results show that the proposed disguised voice recognition method can better fit the feature distribution, and significantly improve the classification effect and recognition rate.
