inter class mllr for speaker adaptation

Inter Class MLLR for Speaker Adaptation

Presenter : 陳彥達

Reference

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

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

Introduction Why do we use MLLR ?

Simple idea Few unknown parameters Sparse adaptation data Rapid adaptation

Introduction(2)

i all Gaussian

,

i Class 1

,

i Class 2

)(ˆ if

)(ˆ 1 ii f

)(ˆ 2 ii f

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

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.

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

)(12 g

)())(()(ˆ )12(11212 iiii fgff

)(ˆ 1 ii f

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

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

i )(kf

i i

)(kf

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

Inter-class function training ,

i class m

, i class n

let

then

, i class n

mnimnimn dTg )(

mimimi bAf )(ˆ

ninini bAf )(ˆ

mmnimnm

mimnmimnmi

bdTA

bgAgf

)(

)())((ˆ

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 smsmnimnms

si bdTA ,,

)( )(ˆ

),( ,, msms bA

∴

mnimnms

si dTbA

ms )ˆ( ,

)(1

,

Inter-class function training(3)

Let

we use the equation above and training data for all speakers to obtain and by conventional MLLR.

)ˆ(ˆ ,)(1),(

, mss

ims

i bAms

mnimnms

i dT ),(ˆ

mnT mnd

Experiments Mean-square error from simulated estimates of G

aussian means

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

Experiments(3)