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Robust Feature Extraction for Mobile-Based Speech Emotion Recognition System

  • Kang-Kue Lee
  • Youn-Ho Cho
  • Kyu-Sik Park
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)

Abstract

In this paper, we propose a robust feature extraction method for mobile-based speech emotion recognition system. A query speech signal is captured by a cellular phone in the real mobile environment. A major problem in this environment is distortions contained in the features of the query sound due to the mobile network and environmental noise. In order to alleviate these noises, a signal subspace noise reduction algorithm is applied. Then a robust feature extraction method called SFS feature optimization is implemented to improve and stabilize the system performance. The proposed system has been tested with cellular phones in the real world and it shows about 73% of average classification success rate with Fuzzy SVM classifier.

Keywords

Speech Signal Emotion Recognition Cellular Phone Speech Emotion Sequential Forward Selection 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kang-Kue Lee
    • 1
  • Youn-Ho Cho
    • 1
  • Kyu-Sik Park
    • 1
  1. 1.Division of Information and Computer ScienceDankook UniversitySeoulKorea

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