Inter Class MLLR for Speaker Adaptation

1. Inter Class MLLR for Speaker Adaptation Presenter : 陳彥達

2. Reference • Sam-Joo Doh and Richard M. Stern, “Inter-Class MLLR for Speaker Adaptation”, ICASSP 2000.

3. Outline • Introduction • Concept of inter-class MLLR • Inter-class function training • Experiments

4. Introduction • Why do we use MLLR ? • Simple idea • Few unknown parameters • Sparse adaptation data • Rapid adaptation

5. Introduction(2) i all Gaussian , i Class 1 , i Class 2

6. Introduction(3) • Single-class MLLR • All parameters use the same transformation in adaptation • More reliable for small amount of adaptation data • Multi-class MLLR • Parameters in different classes use different transformation in adaptation • More reliable for large amount of adaptation data

7. Introduction(4) • Shortcoming of conventional MLLR • The number of classes should be carried out according to the amount of adaptation data. • Classes are independent in multi-class MLLR, so some parameters may not be adapted. • Main idea of inter-class MLLR • Use correlation between different classes to compensate for the shortcoming mentioned above.

8. Concept of inter-class MLLR • Inter-class function • The relation between different classes if is the inter-class function between class 1 and class 2 , i class 1 , i class 2

9. Concept of inter-class MLLR(2) • Model setup steps • Define multiple classes ( say class 1～n ) • Find inter-class functions between each class • Adaptation steps • Choose a target class which is going to be adapted ( say class k ) • Rank other classes according to their “closeness” to the target class

10. Concept of inter-class MLLR(3) • If adaptation data ( i target class k ) coming, use conventional MLLR to find • If adaptation data ( i target class k ) coming, use inter-class function to convert as the adaptation data in class k, and then use conventional MLLR to find • Repeat above steps until all classes are adapted

11. Concept of inter-class MLLR(4) • Adaptation data are selected from classes of decreasing proximity to the target class until there are sufficient data to estimate the target function. • Limit cases • no neighboring classes used → conventional multi-class MLLR • All neighboring classes used → conventional single-class MLLR

12. Inter-class function training , i class m , i class n let then , i class n

13. Inter-class function training(2) Assuming we have training data of R speakers. We use these data to train , for each class for each speaker. ie. s={1,2,…,R}, m={1,2,…,n}. , i class n, for class m for speaker s ∴

14. Inter-class function training(3) Let we use the equation above and training data for all speakers to obtain and by conventional MLLR.

15. Experiments • Mean-square error from simulated estimates of Gaussian means

16. Experiments(2) • Word error rate for different types of MLLR • 25 training speakers • 13 phonetic-based regression classes • 10 testing speakers, 20 sentences per speaker for testing, 5 sentences per speaker for adaptation • use all the neighbor classes to estimate each target class • Silence and noise phones are not adapted

17. Experiments(3)