專題研究 (4) HDecode_live Prof. Lin-Shan Lee, TA. Yun-Chiao Li 1

Additional Information about Kaldi Part 1 2

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 3

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 4

Simple Live Recognition System (HDecode_live) Part 2 5

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) 6 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  (x86 only!!)  Download /share/HDecode_live/  to C:\cygwin\home\youraccount\HDecode_live  leave all the options default and click next 7

Lecture AM / tiedlist am.lecture.speaker- dependent.mmf / tiedlist.news LM trained by yourself Lexicon lexicon.lecture 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  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 9

Acoustic Model Example 10 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 11

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) 12

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 13

Some hint  If you have any problem training LM:  scripts are here: /share/scripts/ 14