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GMER

GMER. Qi Li and Biing-Hwang Juang. Reference. ICASSP2002 – A New Algorithm for Fast Discriminative Training ICASSP2003 – Fast Discriminative Training for Sequential Observations with Application to Speaker Identification

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GMER

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  1. GMER Qi Li and Biing-Hwang Juang

  2. Reference • ICASSP2002 –A New Algorithm for Fast Discriminative Training • ICASSP2003–Fast Discriminative Training for Sequential Observations with Application to Speaker Identification • ICASSP2004–Discovering Relations among Discriminative Training Objectives

  3. Origin of “GMER” • ICASSP2002  fast MER • ICASSP2003  string-based MER • ICASSP2004  generalized MER

  4. Decision Making

  5. MAP  MER

  6. Derivation of MCE

  7. MCE

  8. GMER

  9. GMER

  10. GMER

  11. GMER

  12. GMER

  13. GMER

  14. Algorithm • 1. Initialize all models parameters by MLE • 2. Compute • 3. Determine • 4. Estimate • 5. Calculate the likelihood, if the performance is improved, keep the new model and goto Step2, otherwise break; • 6.Repeate the above procedure for all models.

  15. Aggregate a Posteriori (AAP)

  16. Experiments • Three-Class Classification: • Generate three classes of 2-dimensional data.

  17. Experiments • Three-Class Classification: • After MLE

  18. Experiments • Three-Class Classification: • After GMER

  19. Experiments • Three-Class Classification: • Accuracy

  20. Experiments • Three-Class Classification: • Values of the objective stop

  21. Experiments • Vowel Classification: • Peterson-Barney vowel • 10 vowels (classes) × 76 spks × 2 tokens

  22. Experiments • Speaker Identification: • 11 spks • 60 secs for training, 30-40 secs for testing • 2000 NIST • 12-D MFCC

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