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 Multi-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 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)