Speech recognition using neural networks - Chapter 5 doc
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 1992); but it was later replaced b...
Speech recognition using neural networks - Chapter 2 docx
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2. Review of Speech Recognition
22
2.3.3. ... /ts/
2. Review of Speech Recognition
26
2.3.4. Limitations of HMM...
Speech recognition using neural networks - Chapter 1 pot
... volumes, and so on,
x
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 152 1 3-3 890
Submitted ... that neural networks can indeed form the basis for a general pur-
pose speech recognition system, and that neural networks offer some clear advantages over
co...
Speech recognition using neural networks - Chapter 3 potx
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3. Review of Neural Networks
46
By ... that the neural network may be simulated on a conventional computer, rather than imple-
mented directly in hardware...
Speech recognition using neural networks - Chapter 4 pps
... 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; the speech frames then ... 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...
Speech recognition using neural networks - Chapter 6 pps
... HMMs using a single gaussian mixture, vs. LPNN.
perplexity
System 7 111 402
HMM-1 55 %
HMM -5 96% 70% 58 %
HMM-10 97% 75% 66%
LVQ 98% 80% 74%
LPNN 97% 60% 40%
Table 6.4: Word accuracy of HMM-n with ... (99%)
2 49 /50 (98%) 20/20 (100%) 229/229 (100%)
3 50 /50 (100%) 20/20 (100%) 229/229 (100%)
924 1 106/118 (90%) 55 /60 (92%) 855 /900 ( 95% )
2 116/118 (98%) 58 /60 (97%) 886/900 (98%)...
Speech recognition using neural networks - Chapter 7 pdf
... 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 that have been trained as speech ... helpful.
0
10
20
30
40
50
60
70
80
word accuracy (%)
0 1 2 3 4 5 6 7 8 9 10
epochs
PLP input normalization (May6)
stdev 2
stdev 1.0
stdev .5
stdev . 25
stdev .1 25
No n...
Speech recognition using neural networks - Chapter 8 potx
... in Chapter 7 were developed on this database,
and were never applied to the Conference Registration database.
perplexity test on training set
System 7 111 402(a) 402(b) 111
HMM-1 55 %
HMM -5 96% ... 402(b) 111
HMM-1 55 %
HMM -5 96% 71% 58 % 76%
HMM-10 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: Comparat...
Speech recognition using neural networks - Chapter 9 pptx
... 1 1-1 5
temporal
constraints 1 2-1 3, 16
dynamics 30, 78, 118
integration 38, 53 , 54 , 56 , 11 1-2 , 13 8-9
modeling 7, 1 2-1 3, 16, 5 6 -5 7, 151 , 154
structure 56
variability 1, 4, 5 6 -5 7
tensor products 57
testing ... 103,
11 9-1 20, 138
state of the art. See speech recognition
static classification 51 , 52 , 5 5- 5 7
statistical models 4, 1 5- 2...
speech recognition using neural networks
... performance. We will see
that neural networks help to avoid this problem.
1.2. Neural Networks
Connectionism, or the study of artificial neural networks, was initially inspired by neuro-
biology, but it ... 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...
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