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Segment-Level Probabilistic Sequence Kernel Based Support Vector Machines for Classification of Varying Length Patterns of Speech

  • Shikha Gupta
  • Veena Thenkanidiyoor
  • Dileep Aroor DineshEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

Abstract

In this work we propose the segment-level probabilistic sequence kernel (SLPSK) as dynamic kernel to be used in support vector machine (SVM) for classification of varying length patterns of long duration speech represented as sets of feature vectors. SLPSK is built upon a set of Gaussian basis functions, where half of the basis functions contain class specific information while the other half implicates the common characteristics of all the speech utterances of all classes. The proposed kernel is computed between the pair of examples, by partitioning the speech signal into fixed number of segments and then matching the corresponding segments. We study the performance of the SVM-based classifiers using the proposed SLPSK using different pooling technique for speech emotion recognition and speaker identification and compare with that of the SVM-based classifiers using other kernels for varying length patterns.

Keywords

Feature Vector Speech Signal Gaussian Mixture Model Speaker Recognition Speaker Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shikha Gupta
    • 1
  • Veena Thenkanidiyoor
    • 2
  • Dileep Aroor Dinesh
    • 1
    Email author
  1. 1.School of Computing and Electrical EngineeringIndian Institute of Technology MandiMandiIndia
  2. 2.Department of Computer Science and EngineeringNational Institute of Technology GoaPondaIndia

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