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Support vector data description for spoken digit recognition

Tavanaei, A ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. Publisher: 2012
  3. Abstract:
  4. A classifier based on Support Vector Data Description (SVDD) is proposed for spoken digit recognition. We use the Mel Frequency Discrete Wavelet Coefficients (MFDWC) and the Mel Frequency cepstral Coefficients (MFCC) as the feature vectors. The proposed classifier is compared to the HMM and results are promising and we show the HMM and SVDD classifiers have equal accuracy rates. The performance of the proposed features and SVDD classifier with several kernel functions are evaluated and compared in clean and noisy speech. Because of multi resolution and localization of the Wavelet Transform (WT) and using SVDD, experiments on the spoken digit recognition systems based on MFDWC features and SVDD with weighted polynomial kernel function give better results than the other methods
  5. Keywords:
  6. Machine learning ; Mel frequency discrete wavelet transform ; One-class learning ; Pattern recognition ; Speech recognition ; Support vector data description ; Accuracy rate ; Digit recognition ; Feature vectors ; Kernel function ; Mel-frequency cepstral coefficients ; Mel-frequency discrete wavelet coefficients ; Multi-resolutions ; Noisy speech ; Weighted polynomials ; Discrete wavelet transforms ; Learning systems ; Signal processing
  7. Source: BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing ; 2012 , Pages 32-37 ; 9789898425898 (ISBN)
  8. URL: http://www.scitepress.org/DigitalLibrary/ProceedingsDetails.aspx?ID=o+gjvTSz+O4=&t=1