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The universal speech attributes for speaker verification (SV) are addressed in this paper. The aim of this work is to exploit fundamental characteristics across different speakers within the deep neural network (DNN)/i-vector framework. The manner and place of articulation form the fundamental speech attribute unit inventory, and new attribute units for acoustic modelling are generated by a two-step...
The universal speech attributes to speaker verification (SV) is addressed in this paper. The manner and place of articulation form the universal attribute unit inventory, and deep neural network (DNN) is used as acoustic model. Two methods to generate the attribute units are proposed in this paper: one is that the manner and place of articulation are directly combined to generate more robust universal...
The widely adopted i-vector performances well in text-independent speaker verification with long speech duration. How to integrate the state-of-the-art i-vector framework into the text-prompted speaker verification is addressed in this paper. To take advantage of the lexical information and enhance the performance for speaker verification with random digit sequences, this paper proposes to extract...
The total variability factor extractor followed by the probability linear discriminant analysis (PLDA) has been the state of art algorithm in text-independent speaker verification. In this paper, we use the Support Vector Machine (SVM) to replace PLDA. The low dimensional i-vectors of the total variability system are used as the inputs of the SVM, and the cosine kernel function is adopted to achieve...
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