Proceedings ArticleDOI
Text-independent Speaker Identification Using Fisher Discrimination Dictionary Learning Method
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TLDR
Experimental results show that the proposed Fisher discrimination dictionary learning method outperforms the Sparse Representation Classifier used for text-independent speaker recognition in both clean and noisy condition.Abstract:
In last decades, text-independent speaker recognition is a hot research topic attracted many researchers. In this paper, we proposed to apply the Fisher discrimination dictionary learning method to identify the text-independent speaker recognition. The feature used in classification is the Gaussian Mixture Model super vector. The proposed method is evaluated with public ally available dataset TIMIT. Experimental results show that the proposed method outperforms the Sparse Representation Classifier used for text-independent speaker recognition in both clean and noisy condition.read more
Citations
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Journal ArticleDOI
An Algorithm of Speaker Clustering Based on Model Distance
TL;DR: An algorithm based on Model Distance (MD) for spectral speaker clustering is proposed to deal with the shortcoming of general spectral clustering algorithm in describing the distribution of signal source.
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Sustainable Urbanization in the China‐Indochinese Peninsula Economic Corridor
Dong Jiang,Jingying Fu,Gang Lin +2 more
TL;DR: In this paper, the authors presented a general overview of urbanization procession in this region and monitored the spatiotemporal dynamics of the urban environment; the second objective is to present the multiple driving force factor analysis for urban development in countries of the China Indochinese Peninsula Economic Corridor using statistical models.
References
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Robust Face Recognition via Sparse Representation
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Stable signal recovery from incomplete and inaccurate measurements
TL;DR: In this paper, the authors considered the problem of recovering a vector x ∈ R^m from incomplete and contaminated observations y = Ax ∈ e + e, where e is an error term.
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Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: If the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program.
Posted Content
Stable Signal Recovery from Incomplete and Inaccurate Measurements
TL;DR: It is shown that it is possible to recover x0 accurately based on the data y from incomplete and contaminated observations.
Posted Content
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: In this article, it was shown that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal $f \in {\cal F}$ decay like a power-law, then it is possible to reconstruct $f$ to within very high accuracy from a small number of random measurements.