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Spectral subtraction in model distance maximizing framework for robust speech recognition

BabaAli, B ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/ICOSP.2008.4697210
  3. Publisher: 2008
  4. Abstract:
  5. This paper has presented a novel discriminative parameters calibration approach based on the Model Distance Maximizing (MDM) to improve the performance of our previous proposed robustness method named spectral subtraction (SS) in likelihoodmaximizing framework. In the previous work, for adjusting the spectral over-subtraction factor of SS, conventional ML approach is used that only utilizes the true model without considering other confused models. This makes it very probably to reach a suboptimal solution. While in MDM, by maximizing the dissimilarities among models, the performance of our speech recognizer-based spectral subtraction method could be further improved. Experimental results based on FarsDat database have demonstrated that MDM approach outperformed ML in term of recognition accuracy. © 2008 IEEE
  6. Keywords:
  7. FARSDAT ; Recognition accuracy ; Robust speech recognition ; Spectral subtraction methods ; Spectral subtractions ; Speech recognizer ; Suboptimal solution ; Optimization ; Signal processing ; Speech recognition
  8. Source: 2008 9th International Conference on Signal Processing, ICSP 2008, Beijing, 26 October 2008 through 29 October 2008 ; 2008 , Pages 627-630 ; 9781424421794 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4697210