Text-Independent Speaker Verification
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Presentation slides discussing the theory and empirical results of a text-independent speaker verification system I developed based upon classification of MFCCs. Both mininimum-distance classification and least-likelihood ratio classification using Gaussian Mixture Models were discussed.
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Text-Independent Speaker Verification
- 1. Speaker Recognition Cody A. Ray ECES 435 Final Project March 11, 2010
- 2. Speaker Recognition Speaker Identification Speaker Verification Text Dependent Text Independent Text Dependent Text Independent
- 4. LPCC
- 5. MFCC
- 6. Glottal Flow Derivative
- 8. Min Distance
- 9. DTW
- 11. GMM
- 12. HMM
- 13. Minimum Distance
- 15. Maximum a posteriori
- 18. System 1: Minimum-Distance Average of mel-cepstral features for test and training data
- 19. Minimum-Distance Classifier Mean-squared difference between average testing and training feature vectors
- 20. System 2: Gaussian Mixture Model Multivariate Normal Distribution
- 22. GMM Speaker Recognition System Target Model Feature Vectors Imposter 1 Imposter 2
- 24. Experiments 8 Speakers (4 Male, 4 Female) 2 Sentences Each Don’t ask me to carry an oily rag like that She had your dark suit in greasy wash water all year “Rag” used for training, “suit” for testing
- 25. Results
- 26. Results
- 27. Results Threshold = 0.12 Accuracy = 91%
- 28. Results Threshold = 0.11 Accuracy = 91%
- 29. Conclusions Accuracy isn’t terrible, but room to improve Threshold tradeoff false-negatives vs. false-positives DON’T use Minimum-Distance classifier for text-independent authentication systems
- 30. Future Work Implement LLR Classifier using GMM library Repeat experiment with GMM-based system Compare Min-Distance and GMM results
Editor's Notes
- Analysis window w[n]Discrete STFTWeight the magnitudes of X(n, w) with mel-scale filter bank
- GMM of target speakerGMM for a collection of imposters that we call the background modelRatio of probability that the collection of feature vectors X is from the claimant speaker to the probability that X is not from the claimed speaker (ie, background)
- Mean-squared error (distance metric) is symmetric, so what was used for training/testing doesn’t matter. Output is just transposed with the other order.
- Mean-squared difference between average testing and training feature vectors for each speaker
- Minimize the distances on diagonals (matches)
- 0 false negatives6 false positives
- 1 false negatives5 false positives