Title page for etd-0809116-110726


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URN etd-0809116-110726
Author Chia-ying Chen
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2015
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Speech Emotion Recognition Using Factor Analysis and Identity Vectors
Date of Defense 2016-07-29
Page Count 58
Keyword
  • i-vector
  • Speech Emotion Recognition
  • Vocal Tract Length Normalization
  • Gaussian Mixture Model
  • Support Vector Machine
  • Abstract In this paper, we challenge INTERSPEECH 2009 Emotion open performance sub-Challenge of 5 class problem. Our research evaluates on the well-known FAU Aibo database. We use OpenSMILE toolkit to extract low-level descriptors and compute the delta coefficients. Gaussian Mixture Model (GMM) is popular approach in speaker identification and speaker verification, we use GMM systems to speech emotion recognition. It contains four systems, the first one is simple GMM system. The second one is GMM-UBM system, it resolve the insufficiency of training data. The third is GMM-SVM system, it uses GMM super-vectors as new
    input feature. The fourth is Identity Vectors system (or i-vector system), it uses factor analysis (FA) for GMM super-vectors.
    In the dynamic modeling classifier, we achieve an unweighted average (UA) recall rate of 39.2% in GMM system, and 39.3% in GMM-UBM system, over a baseline of 35.5%. In the static modeling classifier, we use SMOTE and Under-sampling to solve the problem of unbalance data,
    then we achieve the 38.9% UA in GMM-SVM system, and 40.5% in Identity system, it also over a baseline of 38.2%. This paper confirmed that the system in speaker recognition can also use to speech emotion recognition, and it also can improve the result of emotion recognition accuracies.
    Advisory Committee
  • Chung-hsien Wu - chair
  • Hsin-Min Wang - co-chair
  • Chia-Ping Chen - advisor
  • Files
  • etd-0809116-110726.pdf
  • indicate access worldwide
    Date of Submission 2016-09-09

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