Speaker Discrimination Using Several Classifiers and a Relativistic Speaker Characterization

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• 关键词：Speaker discrimination ; Speaker verification ; Relativistic speaker characteristic ; PCA reduction ; Classification models
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9680
• 期：1
• 页码：203-212
• 全文大小：259 KB
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• 作者单位：Siham Ouamour (19)
  Zohra Hamadache (19)
  Halim Sayoud (19)
  
  19. USTHB University, Algiers, Algeria
  
• 丛书名：Image and Signal Processing
• ISBN：978-3-319-33618-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9680

文摘

Automatic Speaker Discrimination consists in checking whether two speech signals belong to the same speaker or not. It is often difficult to decide what could be the best classifier to use in some specific circumstances. That is why, we implemented nine different classifiers, namely: Linear Discriminant Analysis, Adaboost, Support Vector Machines, Multi-Layer Perceptron, Linear Regression, Generalized Linear Model, Self Organizing Map, Second Order Statistical Measures and Gaussian Mixture Models. Moreover, a special feature reduction was proposed, which we called Relativistic Speaker Characteristic (RSC). On the other hand we further intensified the feature reduction by adding a second step of feature transformation using a Principal Component Analysis (PCA). Experiments of speaker discrimination are conducted on Hub4 Broadcast-News. Results show that the best classifier is the SVM and that the proposed feature reduction association (RSC-PCA) is extremely efficient in automatic speaker discrimination.