專題研究 (4) HDecode_live
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Transcript of 專題研究 (4) HDecode_live
專題研究 (4)HDecode_live
Prof. Lin-Shan Lee, TA. Yun-Chiao Li
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Additional Information about Kaldi
Part 12
Kaldi – some practices (1/2)
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
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Kaldi – some practices (2/2)
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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Simple Live Recognition System (HDecode_live)
Part 25
Simple Recognition System
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 -a 0.5 (acoustic model weight) -s 8.0 (language model weight) -t 75.0 (beamwidth)
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You can change these parameters and see what will
happen
Setup
Cygwin The purpose to use Cygwin is to simulate
the unix operating system in windows Install Cygwin
http://cygwin.com/setup-x86.exe (x86 only!!)
Download /share/HDecode_live/ to C:\cygwin\home\youraccount\
HDecode_live leave all the options default and click next
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• 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
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
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Acoustic Model Example10
Here is the HMM model for each
phone
Here is the Gaussian mixture model for
each state
Language model training (1/2) 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
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Language model training (2/2) 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)
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Simple Recognition System
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
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Some hint
If you have any problem training LM: scripts are here: /share/scripts/
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