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Model Adaptation Techniques in Machine Learning for Speaker Recognition

This lecture discusses the adaptation of Gaussian Mixture Models (GMM) through Maximum A Posteriori (MAP) and Maximum Likelihood Linear Regression (MLLR) techniques in the context of speaker recognition. It addresses the challenge of having limited labeled data and abundant unlabeled data, exploring ways to leverage this unlabeled data to improve performance. The presentation covers the methodologies for adapting models to new data while ensuring effective parameter estimation. Key applications in identifying speakers based on their unique audio features and the effectiveness of this approach in achieving high accuracy in speaker recognition tasks are also highlighted.

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Model Adaptation Techniques in Machine Learning for Speaker Recognition

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  1. Lecture 20: Model Adaptation Machine Learning April 15, 2010

  2. Today • Adaptation of Gaussian Mixture Models • Maximum A Posteriori (MAP) • Maximum Likelihood Linear Regression (MLLR) • Application: Speaker Recognition • UBM-MAP + SVM

  3. The Problem • I have a little bit of labeled data, and a lot of unlabeled data. • I can model the trainingdata fairly well. • But we always fit training data betterthan testing data. • Can we use the wealthof unlabeled data to do better?

  4. Let’s use a GMM • GMMs to model labeled data. • In simplest form, one mixture component per class.

  5. Labeled training of GMM • MLE estimators of parameters • Or these can be used to seed EM.

  6. Adapting the mixtures to new data • Essentially, let EM start with MLE parameters as seeds. • Expand the available data for EM, proceed until convergence

  7. Adapting the mixtures to new data • Essentially, let EM start with MLE parameters as seeds. • Expand the available data for EM, proceed until convergence

  8. Problem with EM adaptation • The initial labeled seeds could contribute very little to the final model

  9. One Problem with EM adaptation • The initial labeled seeds could contribute very little to the final model

  10. MAP Adaptation • Constrain the contribution of unlabeled data. • Let the alpha terms dictate how much weight to give to the new, unlabeled data compared to the exiting estimates.

  11. MAP adaptation • The movement of the parameters is constrained.

  12. MLLR adaptation • Another idea… • “Maximum Likelihood Linear Regression”. • Apply an affine transformation to the means. • Don’t change the covariance matrices

  13. MLLR adaptation • Another view on adaptation. • Apply an affine transformation to the means. • Don’t change the covariance matrices

  14. MLLR adaptation • The new means are the MLE of the means with the new data.

  15. MLLR adaptation • The new means are the MLE of the means with the new data.

  16. MLLR adaptation • The new means are the MLE of the means with the new data.

  17. Why MLLR? • We can tie the transformation matrices of mixture components. • For example: • You know that the red and green classes are similar • Assumption: Their transformations should be similar

  18. Why MLLR? • We can tie the transformation matrices of mixture components. • For example: • You know that the red and green classes are similar • Assumption: Their transformations should be similar

  19. Application of Model Adaptation • Speaker Recognition. • Task: Given speech from a known set of speakers, identify the speaker. • Assume there is training data from each speaker. • Approach: • Model a generic speaker. • Identify a speaker by its difference from the generic speaker • Measure this difference by adaptation parameters

  20. Speech Representation • Extract a feature representation of speech. • Samples every 10ms. MFCC – 16 dims

  21. Similarity of sounds MFCC2 /s/ /b/ /u/ /o/ MFCC1

  22. Universal Background Model • If we had labeled phone information that would be great. • But it’s expensive, and time consuming. • So just fit a GMM to the MFCC representation of all of the speech you have. • Generally all but one example, but we’ll come back to this.

  23. MFCC Scatter MFCC2 /s/ /b/ /u/ /o/ MFCC1

  24. UBM fitting MFCC2 /s/ /b/ /u/ /o/ MFCC1

  25. MAP adaptation • When we have a segment of speech to evaluate, • Generate MFCC features. • Use MAP adaptation on the UBM Gaussian Mixture Model.

  26. MAP Adaptation MFCC2 /s/ /b/ /u/ /o/ MFCC1

  27. MAP Adaptation MFCC2 /s/ /b/ /u/ /o/ MFCC1

  28. UBM-MAP • Claim: • The differences between speakers can be represented by the movement of the mixture components of the UBM. • How do we train this model?

  29. UBM-MAP training • Supervector • A vector of adapted means of the gaussian mixture components Training Data UBM Training Supervector MAP Held out Speaker N Train a supervised model with these labeled vectors.

  30. UBM-MAP training Training Data UBM Training Supervector Multiclass SVM Training MAP Held out Speaker N Repeat for all training data

  31. UBM-MAP Evaluation UBM Multiclass SVM Supervector MAP Prediction Test Data

  32. Alternate View • Do we need all this? • What if we just train an SVM on labeled MFCC data? Labeled Training Data Multiclass SVM Multiclass SVM Training Test Data Prediction

  33. Results • UBM-MAP (with some variants) is the state-of-the-art in Speaker Recognition. • Current state of the art performance is about 97% accuracy (~2.5% EER) with a few minutes of speech. • Direct MFCC modeling performs about half as well ~5% EER.

  34. Model Adaptation • Adaptation allows GMMs to be seeded with labeled data. • Incorporation of unlabeled data gives a more robust model. • Adaptation process can be used to differentiate members of the population • UBM-MAP

  35. Next Time • Spectral Clustering

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