Speech Project Week 2

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專題研究 (2)
Feature Extraction,
Acoustic Model Training
WFST Decoding
Prof. Lin-Shan Lee, TA. Yun-Chiao Li
Announcement
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You will probably have many questions from today
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Go to ptt2 “SpeechProj”
Your problem can probably help others
Linux Shell Script Basics
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echo “Hello” (print “hello” on the screen)
a=ABC
(assign ABC to a)
echo $a
(will print ABC on the screen)
b=$a.log
(assign ABC.log to b)
cat $b > testfile (write “ABC.log” to testfile)
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指令 -h
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(will output the help information)
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Feature Extraction
02.01.extract.feat.sh
02.02.convert.htk.feat.sh
Feature Extraction - MFCC
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02.01.extract.feat.sh
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Example of MFCC
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02.02.convert.htk.feat.sh
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Hidden Markov Model Toolkit (HTK) is the model we
used to use
In this project, we learn Kaldi
Vulcan provide an interface to convert one to another
Type “bash 02.02.convert.htk.feat.sh”
The feature will then be converted to HTK format
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Acoustic Model Training
03.01.mono0a.train.sh
Acoustic Model
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Hidden Markov Model/Gaussian Mixture Model
3 states per model
Example
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Acoustic model training (1/2)
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When training acoustic model, we need labelled data
material/train.txt
Lacks the information to train
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03.01.mono0a.train.sh
initialized the HMM model with
equally aligning frame to each state
Gaussian Mixture Model (GMM)
accumulation and estimation.
you might want to check “HMM
Parameter Estimation ” in HTK Book,
or “HMM problem 3” in course
Acoustic model training (2/2)
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Refine the alignment in some
specific iterations,
(in variable realign_iters)
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Introduction to WFST
FST
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An FSA “accepts” a set of strings
View FSA as a representation of a possibly infinite set of strings
Start state(s) bold; final/accepting states have extra circle.
This example represents the infinite set {ab, aab, aaab , . . .}
WFST
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Like a normal FSA but with costs on the arcs and final-states
Note: cost comes after “/”, For final-state, “2/1” means finalcost 1 on state 2.
This example maps ab to (3 = 1 + 1 + 1), all else to 1.
WFST Composition
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Notation: C = A B means, C is A composed with B
WFST Component
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HCLG = H。C。L。G
H: HMM structure
C: Context-dependent relabeling
L: Lexicon
G: language model acceptor
Framework for Speech Recognition
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WFST Component
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Where is C ?
(Context-Dependent)
H (HMM)
L(Lexicon)
G (Language Model)
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Training WFST
03.02.mono0a.mkgraph.sh
03.02.mono0a.mkgraph.sh
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Decoding WFST
03.03.mono0a.fst.sh
Decoding WFST (1/2)
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From HCLG we have…
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the relationship from state -> word
We need another WFST, U
Compose U with HCLG, ie, S = U。HCLG
Search the best path(s) on S is the recognition result
Decoding WFST (2/2)
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During decoding, we need to specify the weight respectively
for acoustic model and language model
Split the corpus to Train, Test, Dev set
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Training set used to training acoustic model
Test all of the acoustic model weight on Dev set, and use the best
Test set used to test our performance (Word Error Rate, WER)
03.03.mono0a.fst.sh (1/2)
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03.03.mono0a.fst.sh (2/2)
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Homework
02.01~03.04.sh
To Do
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Copy data into your own directory
 cp
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–r /share/
Execute the following command:
 bash
01.format.data.sh
 bash 02.01.extract.feat.sh
 bash 02.02.convert.htk.feat.sh
…
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Observe the output and report
You might want to check HTK book for acoustic
model training
Some Helpful References
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“使用加權有限狀態轉換器的基於混合詞與次詞
以文字及語音指令偵測口語詞彙” – 第三章
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 https://www.dropbox.com/s/dsaqh6xa9dp3dzw/wfst
_thesis.pdf
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