Skip to main content

Table 2 Word recognition accuracy (%) of the Tucker 3-mode and probabilistic 2-mode based methods

From: Speaker adaptation in the maximum a posteriori framework based on the probabilistic 2-mode analysis of training models

  

Number of

Number of adaptation sentences

Method

(K R ,K D )

free parameters

1

2

3

4

5

Tucker 3-mode

(20, 35)

700

91.84

92.98

93.07

92.99

93.11

 

(20, 38)

760

91.82

92.83

93.11

93.01

93.01

 

(30, 35)

1050

90.77

92.99

93.18

93.09

92.94

 

(30, 38)

1140

90.77

92.86

93.18

93.01

92.86

 

(40, 35)

1400

89.39

92.85

93.11

93.24

93.03

 

(40, 38)

1520

89.16

92.77

93.20

93.14

92.98

 

(50, 35)

1750

87.95

92.34

93.24

93.26

93.13

 

(50, 38)

1900

87.75

92.47

93.27

93.31

93.16

Probabilistic 2-mode

(20, 35)

700

93.07

93.18

93.26

93.27

93.16

 

(20, 38)

760

92.96

93.07

93.03

93.24

93.13

 

(30, 35)

1050

92.98

93.20

93.24

93.24

93.31

 

(30, 38)

1140

92.94

93.33

93.33

93.27

93.31

 

(40, 35)

1400

93.13

93.20

93.39

93.24

93.01

 

(40, 38)

1520

93.14

93.22

93.33

93.20

93.24

 

(50, 35)

1750

93.26

93.35

93.37

93.29

93.29

 

(50, 38)

1900

93.37

93.44

93.42

93.31

93.39

  1. The number of mixture components R=3472·8 and the dimension of acoustic feature vector D=39. The number of free parameters is K R ×K D .