Speech recognition using neural networks - Chapter 2 docx
Speech recognition using neural networks - Chapter 2 docx
... (20 0)
M,A,R,K,E,T
$
M,
M
A,
A
R,
R
K,
K
E,
E
T
$
M
A
,
M
A
R
,
A
R
K
,
R
K
E
,
K
E
T
,
E
T
$
MAR,KET
MA,AR,KE,ET
1087,486 ,25 02, 986,3814 ,27 15
generalized triphone (4000)
MARKET
M
1
,M
2
,M
3
;
A
1
,A
2
,A
3
;
M = 3843 ,22 57,1056;
A = 1894, 124 7,38 52;
senone (4000)
2. Review of Speech Recognition
22
2. 3.3. Variations
There are many variations ... /ts/
2. Review of Sp...
Speech recognition using neural networks - Chapter 1 pot
... volumes, and so on,
x
Speech Recognition using Neural Networks
Joe Tebelskis
May 1995
CMU-CS-9 5-1 42
School of Computer Science
Carnegie Mellon University
Pittsburgh, Pennsylvania 1 521 3-3 890
Submitted ... few chapters of this thesis provide some essential background and a summary of
related work in speech recognition and neural networks:
• Chapter 2 reviews the...
Speech recognition using neural networks - Chapter 3 potx
... as LVQ2, an improvement over the original LVQ training algorithm.
w
1
∆ +ε x w
1
–( )=
w
2
∆ ε x w
2
–( )–=
E
1
2
w
ji
y
i
y
j
j i≠
∑
i
∑
–=
3 .2. Fundamentals of Neural Networks
31
(22 )
from ... ) P x c
i
( ) P c
i
( )
i
∑
=
3 .2. Fundamentals of Neural Networks
29
In drawings of neural networks, units are usually represented by circles. Also, by conven-
tion, input uni...
Speech recognition using neural networks - Chapter 4 pps
... achieving 22 .7% versus 26 .0% error for speaker-dependent recognition,
and 30.8% versus 40.8% error for multi-speaker recognition. Training time was reduced to
a reasonable level by using a 64-processor ... by a simple HMM-based recog-
nizer.
Figure 4.3: Time Delay Neural Network.
Integration
Speech
input
Phoneme
output
B
D
G
B
D
G
4.3. NN-HMM Hybrids
63
and neural networks; th...
Speech recognition using neural networks - Chapter 5 doc
... no English. Janus performs speech trans-
lation by integrating three modules — speech recognition, text translation, and speech gen-
eration — into a single end-to-end system. Each of these modules ... The speech recognition module, for exam-
ple, was originally implemented by our LPNN, described in Chapter 6 (Waibel et al 1991,
Osterholtz et al 19 92) ; but it was later replaced...
Speech recognition using neural networks - Chapter 6 pps
... 19 /20 (95%) 22 8 /22 9 (99%)
2 49/50 (98%) 20 /20 (100%) 22 9 /22 9 (100%)
3 50/50 (100%) 20 /20 (100%) 22 9 /22 9 (100%)
924 1 106/118 (90%) 55/60 ( 92% ) 855/900 (95%)
2 116/118 (98%) 58/60 (97%) 886/900 (98%)
3 ... consid-
ered correctly recognized if it appears among the best K candidates.
Vocab size Rank Testing set Training set
Homophones Novel words
23 4 1 47/50 (94%) 19 /2...
Speech recognition using neural networks - Chapter 7 pdf
... 2 3 4 5
epochs
3600 train, 390 test. (Aug24)
FFT-16
FFT- 32 (with deltas)
PLP -2 6 (with deltas)
LDA-16 (derived from FFT- 32)
7.3. Frame Level Training
121
7.3.4.1. Learning Rate Schedules
The learning ... training of a speech recognizer?”
7.5. Summary
143
7.5. Summary
In this chapter we have seen that good word recognition accuracy can be achieved using
neural networks tha...
Speech recognition using neural networks - Chapter 8 potx
... NN-HMM hybrid systems (first three entries) consistently
outperformed the pure HMM systems (CI-Sphinx and CI-Decipher), using a comparable
number of parameters. This supports our claim that neural ... discussed in Chapter 7 were developed on this database,
and were never applied to the Conference Registration database.
perplexity test on training set
System 7 111 4 02( a) 4 02( b) 111
H...
Speech recognition using neural networks - Chapter 9 pptx
... inhibition 42
layers 2 8-3 0, 38, 62, 107
LDA 10, 24 , 26 , 116, 118, 144, 15 3-1 55
learning 4, 27
learning rate 35, 45, 47, 12 1-1 27 , 144, 153
constant 121 , 126
geometric 122 , 124 , 126
asymptotic 125 , 126
search ... modeling 2 2- 2 3, 26 , 89, 154
frame vs. word 12 1-1 22
phoneme 5 2- 5 6, 60, 63, 98
word 12 1-1 22 , 144, 14 8-1 49
prediction 87, 89,...
speech recognition using neural networks
... own advocates.
Figure 2. 1: Structure of a standard speech recognition system.
Figure 2. 2: Signal analysis converts raw speech to speech frames.
raw
speech
signal
analysis
speech
frames
acoustic
models
frame
scores
sequential
constraints
word
sequence
segmentation
time
alignment
acoustic
analysis
train
train
test
train
raw ... )
i
∑
=
α
j
(t)
t-1 t
α
i
(t-1)
.
.
.
.
a
ij
b
j
(y...
Từ khóa:
- contentbased image categorization and retrieval using neural networks
- automatic steering of ships using neural networks
- automatic speech recognition using matlab download
- automatic speech recognition using matlab pdf
- automatic speech recognition using matlab source code
- using neural networks to control agent threat response
- number plate recognition using svm and neural networks