Transcript Speech Project Week 6

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專題研究 (4)
HDecode_live
Prof. Lin-Shan Lee, TA. Yun-Chiao Li
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Part 1
Additional Information about Kaldi
Kaldi – some practices (1/2)
3

In 03.01:
 Try
to modify the total number of Gaussian by
modifying “totgauss”

In 04.01:
 Try
to modify the number of leaves of decision tree by
modifying “numleaves”
 Try to modify the total number of Gaussian by
modifying “totgauss”

run through the scripts and see the changes in
performance and the optimal weight
Kaldi – some practices (2/2)
4

Some tips:
you can change “numleaves” up to around 4500
 keeping the number of Gaussian less than 20 times of
“numleaves” is more stable


Try to modify other parameters if you have time:
numiters: number of iterations
 realign_iters: those iterations to realign the feature to state

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Part 2
Simple Live Recognition System (HDecode_live)
Simple Recognition System
6


Make sure the microphone is functional
和 HDecode 用法相同 (hdecode.sh)
 HDecode
-> Hdecode_live
 Make sure HDecode, record, HCopy is under the same
directory
 Work on cygwin
 Use bi-gram language model
You can change these
 -a 0.5 (acoustic model weight)
parameters and see what
will happen
 -s 8.0 (language model weight)
 -t 75.0 (beamwidth)
Setup
7

Cygwin
 The
purpose to use Cygwin is to simulate the unix
operating system in windows

Install Cygwin
 http://cygwin.com/setup-x86.exe
 Download
(x86 only!!)
/share/HDecode_live/
 to C:\cygwin\home\youraccount\HDecode_live
 leave all the options default and click next
Lecture
AM / tiedlist
am.lecture.speakerdependent.mmf
/ tiedlist.news
LM
Lexicon
trained by
yourself
lexicon.lectur
e
News
AM / tiedlist
am.news.mmf /
tiedlist.news
LM
trained by
yourself
Lexicon
lexicon.news
• There are two sets of
recognition system
• Lecture
• AM here is
trained by Prof.
Lee’s sound
• News
• AM here is
trained by several
news reporter’s
sound
• The News system provides
better performance
Acoustic Model
9


Training AM by HTK is time consuming
We’ve trained it for you



final.mmf is the speaker dependent AM trained by Prof. Lee’s voice
Therefore, it is suitable to recognize the professor’s voice
it is the same as what we used in Kaldi
Acoustic Model Example
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Here is the HMM
model for each phone
Here is the Gaussian mixture
model for each state
Language model training (1/2)
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
remove the first column in material/train.text, and
rename it as train.lecture
 hint:


vim visual block + “d”
train.lecture:

OKAY [A66E] [A655][A6EC] [A6AD]

[B36F][AAF9][BDD2] [AC4F] [BCC6][A6EC] [BB79][ADB5][B342][B27A] EMPH_A

[A8BA] [B36F][AC4F] [A8E2] [ADD3] [A5D8][AABA]
Change encoding:

/share/tool/chencoding -f ascii -t utf8 train.lecture > train.lecture.utf8

OKAY 好 各位 早

這門課 是 數位 語音處理 EMPH_A

那 這是 兩 個 目的
Language model training (2/2)
12

We prepare another language model too
 Use
the news corpus to train language model
 copy it to your folder
 cp
/share/corpus/train.* .
 cp /share/corpus/lexicon.* .

/share/tool/ngram-count
 -order
2
(you can modify it from 1~3!)
 -kndiscount
(modified Kneser-Ney)
 -text train.lecture
(training data, also try train.news!)
 -vocab lexicon.lecture (lexicon, also try lexicon.news!)
 -lm languagemodel (output language model name)
Simple Recognition System
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

Execute Cygwin Terminal in Windows
Edit hdecode.lecture.sh/hdecode.news.sh
 change





the language model to your’s
Execute “bash hdecode.lecture.sh/hdecode.news.sh”
Wait until “Ready…” appears in the terminal
Click “Enter” and say something
Click “Enter” again and wait for the result
Type “exit” if you want to leave
Some hint
14

If you have any problem training LM:
 scripts
are here: /share/scripts/