Speech recognition using neural networks - Chapter 1 pot
Speech recognition using neural networks - Chapter 1 pot
... volumes, and so on,
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Speech Recognition using Neural Networks
Joe Tebelskis
May 19 95
CMU-CS-9 5 -1 42
School of Computer Science
Carnegie Mellon University
Pittsburgh, Pennsylvania 15 21 3-3 890
Submitted ... up to large speech recogni-
tion tasks.
This thesis demonstrates that neural networks can indeed form the basis for a general pur-
pose speech recognition sys...
Speech recognition using neural networks - Chapter 3 potx
... and w
2
, then we move w
1
toward x, and w
2
away from x:
(34)
3.3 .1. 2. Recurrent Networks
Hopfield (19 82) studied neural networks that implement a kind of content-addressable
associative memory. ... x
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Speech recognition using neural networks - Chapter 8 potx
... 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 11 1 402(a) 402(b) 11 1
HMM -1 55%
HMM-5 96% ... 11 1
HMM -1 55%
HMM-5 96% 71% 58% 76%
HMM -1 0 97% 75% 66% 82%
LPNN 97% 60% 41%
HCNN 75%
LVQ 98% 84% 74% 61% 83%
TDNN 98% 78% 72% 64%
MS-TDNN 98% 82% 81% 70% 85%
Table 8 .1: Com...
Speech recognition using neural networks - Chapter 2 docx
... cell.
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0.4
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.17 64
j=0
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t=0
.42 ... taken, from time 1 to T:
Figure 2 .12 : The backward pass recursion.
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Speech recognition using neural networks - Chapter 4 pps
... x( )⋅=
51
4. Related Research
4 .1. Early Neural Network Approaches
Because speech recognition is basically a pattern recognition problem, and because neural
networks are good at pattern recognition, ... 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...
Speech recognition using neural networks - Chapter 5 doc
... was developed in conjunction with the Janus
Speech- to -Speech Translation system at CMU (Waibel et al 19 91, Osterholtz et al 19 92,
Woszczyna et al 19 94). While a full discussion of Janus is beyond ... 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. Ea...
Speech recognition using neural networks - Chapter 6 pps
... 20/20 (10 0%) 229/229 (10 0%)
3 50/50 (10 0%) 20/20 (10 0%) 229/229 (10 0%)
924 1 106 /11 8 (90%) 55/60 (92%) 855/900 (95%)
2 11 6 /11 8 (98%) 58/60 (97%) 886/900 (98%)
3 11 7 /11 8 (99%) 60/60 (10 0%) 8 91/ 900 ... later in this chapter.
P
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, X
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Speech recognition using neural networks - Chapter 7 pdf
... labeling.
Figure 7 .14 : A 3-state phoneme model outperforms a 1- state phoneme model.
80
82
84
86
88
90
92
94
96
98
10 0
word accuracy (%)
0 1 2 3 4 5
epochs
1- state vs 3-state models
1 state per phoneme
1 3 states ... 0.25
learnRate *= 0.5
learnRate *= 1. 0
learnRate *= 2.0+
.00 01
.00 01
.00 01
.0003
.0 010
.0090
.0030
.00 21
.0 015
.0 010
.0007
.0005
7.4. Word Level Tra...
Speech recognition using neural networks - Chapter 9 pptx
... 10 , 24, 26, 11 6, 11 8, 14 4, 15 3 -1 55
learning 4, 27
learning rate 35, 45, 47, 12 1- 1 27, 14 4, 15 3
constant 12 1, 12 6
geometric 12 2, 12 4, 12 6
asymptotic 12 5, 12 6
search 12 2 12 7, 14 4, 15 0, 15 3
factors ... 16 , 61, 80, 94, 13 8 -1 43
recognition 1 4 -1 9, 5 5-5 6, 61, 8 4-6 , 13 8,
14 3 -1 44, 14 8 -1 49
spotting 6 9-7 1
transition pen...
speech recognition using neural networks
... cell.
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.17 64
j=0
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.42 ... taken, from time 1 to T:
Figure 2 .12 : The backward pass recursion.
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