Abstract
Extracting speaker’s personalized feature parameters is vital for speaker recognition. Only one kind of feature cannot fully reflect the speaker’s personality information. In order to represent the speaker’s identity more comprehensively and improve speaker recognition rate, we propose a speaker recognition method based on the fusion feature of a deep and shallow recombination Gaussian supervector. In this method, the deep bottleneck features are first extracted by Deep Neural Network (DNN), which are used for the input of the Gaussian Mixture Model (GMM) to obtain the deep Gaussian supervector. On the other hand, we input the Mel-Frequency Cepstral Coefficient (MFCC) to GMM directly to extract the traditional Gaussian supervector. Finally, the two categories of features are combined in the form of horizontal dimension augmentation. In addition, when the number of speakers to be recognized increases, in order to prevent the system recognition rate from falling sharply, we introduce the optimization algorithm to find the optimal weight before the feature fusion. The experiment results indicate that the speaker recognition rate based on the feature which is fused directly can reach 98.75%, which is 5% and 0.62% higher than the traditional feature and deep bottleneck feature, respectively. When the number of speakers increases, the fusion feature based on optimized weight coefficients can improve the recognition rate by 0.81%. It is validated that our proposed fusion method can effectively consider the complementarity of the different types of features and improve the speaker recognition rate.
Highlights
Over the last two decades, with the rapid development of artificial intelligence, voiceprint, iris, fingerprint, face and other biometrics have been of wide concern [1,2,3].Speech is the most common way to communicate and convey information in people’s daily life
In order to take into account the between different hierarchical features, we propose a novel fusion model to form acomplementari new between we propose a novel fusion model to form a ne Gaussian supervector fordifferent speakerhierarchical recognition.features, (3) We propose a speaker recognition system
We propose a speaker recognition sy based on optimization weight coefficient, which improves the robustness of the system
Summary
Over the last two decades, with the rapid development of artificial intelligence, voiceprint, iris, fingerprint, face and other biometrics have been of wide concern [1,2,3]. In order to fully express the features of speech signals and take advantage of each model, some studies have proposed different fusion strategies to complete speaker recognition in recent years. We propose a new speaker recognition method based on the fusion of by effe aspects, the speaker’s characteristics can be more comprehensively represented deep and shallow. We propose a new speaker methodobtained based on the fusi from the inputofspeech signal, and Gaussian DNN is used toInobtain the bottleneck features, deep and shallow supervector. This method, the MFCC is firstly obtain which are usedfrom to acquire thespeech deep Gaussian supervector.
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