1. Using HTK for Phoneme-level Recognition in the developed Filipino Phonetically-balanced words
Arnel Fajardo, student (“Hak Seng”)
Under Professor Yoon-joong Kim (“Paksa”)

2. Phoneme Recognition unit
No. of phonemes =36
Recognized phonemes=33
Set of phonemes
Vowels
/a/ /e/ /i/ /o/ /u/
Consonants
/b/ /k/ /d/ /g/ /h/ /l/ /m/ /n/ /ŋ/ /p/ /r/ /s/ /t/ /w/ /y/
Diphtongs
/iw/ /ay/ /aw/ /oy/ /ey/ /uy/
Additions:
/p:/ /b:/ /m:/ /t:/ /d:/ /n:/ /s:/ /l:/ /k:/ /g:/

3. Speech Recognition Process
HTK using Recognition Process

4. Preparation of the Data
Speech Data for Training and Testing -Data ( wave file) 50 sets ( 25 male and 25 female) 257 words ( 2-word list) Training data 40 sets ( 20 male and 20 female) 212 words (3-word list) Test Data Speaker Dependent: 20 sets each for male and female Speaker independent: 5 sets each for male and female Location: Test_htk_samples/Htk_phoneme_2/Data/pbw_list_2/speakernumber/PBW2/speech file Feature of Speech Data: -wave *.wav -16Khz, 16 bit, linear PCM -Phonetically Balanced Words (PBW)

5. Preparation of the Data
Creating phoneme in an MLF(Master Label File)
HLEd –n modelList/monoList -l * -d dic/phoneDict i mlfs/phones.mlf scripts/mono.led mlfs/words.mlf EX
Edit Script
(mono.led)
Phone Level Transcription
(phones.mlf)
Word Level Transcription
(words.mlf)
HLEd
Phone Level Model List
(monoList)
Dictionary
(phoneDict)

6. Preparation of the Data
[input] Creating a modellist
Filename: modelList/wordList
contents
[input] MLF(Creating a file)
file: mlfs/words.mlf
contents
sil
aba'y
agad
akin
aking
ako'y
alam
alon
amin
aming
anak
anong
apat
araw
atin
ating
ayaw …
#!MLF!#
"*/PBW2001.lab"
sil
aba'y .
"*/PBW2002.lab"
agad
"*/PBW2003.lab"
akin …

7. Preparation of the Data
[output]phoneme model list
File: modelList/monoList
Contents
[output]phoneme-level-file
File: mlfs/phones.mlf
Contents

8. Preparation of the Data
Parameter Characteristics Extraction
HCopy –C configs/Hcopy.config –S scripts/Hcopy.scp

9. Preparation of the Data
[input]scripts/HCopy.scp
[input]configs/HCopy.config
# Coding parameters
SOURCEKIND = WAVEFORM
SOURCEFORMAT = WAVE
SOURCERATE = 625
TARGETKIND = MFCC_0
TARGETRATE =
SAVECOMPRESSED = T
SAVEWITHCRC = T
WINDOWSIZE =
USEHAMMING = T
PREEMCOEF = 0.97
NUMCHANS = 26
CEPLIFTER = 22
NUMCEPS = 12
ENORMALISE = F

10. Preparation of the Data
Create file list for testing
File: scripts/test.scp
Create file list for training
File: scripts/train.scp

11. Acoustic Model Creation
Topology used
7 states left-to-right HMM for PBW 2

12. Acoustic Model Creation
General Model Generation
HCompV -C configs/config -f 0.01 –m –S scripts/train.scp –M monoHmms/m0 monoHmms/proto

13. Acoustic Model Creation
[input]
monoHmms/proto

14. Acoustic Model Creation
[input] scripts/train.scp
[input] Configs/config
Start of config file modification
TARGETKIND : MFCC_0_D_A
# Coding parameters
NONUMSCAPES = T
TARGETKIND = MFCC_0_D_A
TARGETRATE =
SAVECOMPRESSED = T
SAVEWITHCRC = T
WINDOWSIZE =
USEHAMMING = T
PREEMCOEF = 0.97
NUMCHANS = 26
CEPLIFTER = 22
NUMCEPS = 12
ENORMALISE = F

15. Acoustic Model Creation
[output] monoHmms/m0/vFloor

16. Acoustic Model Creation
[output] monoHmms/m0/proto

17. Acoustic Model Creation
Phoneme units model
Written macro file (monoHmms/m0/macros)
vFloor The contents of the file created by adding :
" ~o <MFCC_0_D_A><VECSIZE>39 <MFCC_D_A_0>”

18. Acoustic Model Creation
Model phoneme units
Hmms/m0/hmmdefs
To recognize all phoneme units of hmm that defines the
model
~h “a”
<BEGINHMM> ~
<ENDHMM>
~h “b”
<BEGINHMM> ~
<ENDHHMM>

19. Training: Embedded Training
HERest –C configs/config –l mlfs/phones.mlf –S scripts/train.scp H monoHmms/m0/macros –H monoHmms/m0/hmmdefs M monoHmms/m1 modellist/monoList
Training Files listed in
(train.scp)
Configuration File
(config)
Hmm0(m0)
/macros
/hmmdefs
HERest
Hmm1(m1)
/macros
/hmmdefs
Phone Level Transcription
(phones.mlf)
HMM List
(monoList)

20. Training: Embedded Training
[input]monoHmms/m0/hmmdefs
[input] scripts/train.scp
[input]monoHmms/m0/macros
[input]mlfs/phones.mlf

21. Training : Embedded Training
Evaluation
HERest –C configs/config –l mlfs/phones.mlf –S scripts/train.scp H monoHmms/m1/macros –H monoHmms/m1/hmmdefs M monoHmms/m2 modellist/monoList
HERest –C configs/config –l mlfs/phones.mlf –S scripts/train.scp H monoHmms/m2/macros –H monoHmms/m2/hmmdefs M monoHmms/m3 modellist/monoList

22. Training: Embedded Training
Re-evaluation
HERest –C configs/config –l mlfs/phones.mlf –S scripts/train.scp H monoHmms/m3/macros –H monoHmms/m3/hmmdefs M monoHmms/m4 modellist/monoList
HERest –C configs/config –l mlfs/phones.mlf –S scripts/train.scp H monoHmms/m4/macros –H monoHmms/m4/hmmdefs M monoHmms/m5 modellist/monoList

23. Recognition Preparation for recognition
Pronunciation dictionary(dic/phonedict)
Word models
Word : target word recognition
[models : hmm model list

24. Recognition Preparation for recognition
Creating a grammar file (dic/pbwGram)
Grammar Rules for creating grammar file

25. HParse –C configs/config dic/pbwGram dic/tag_Net
Recognition
Preparation for Recognition-Recognition Network creation
HParse dic/pbwGram dic/tag_Net
HParse –C configs/config dic/pbwGram dic/tag_Net

26. Recognition
[input] dic/pbwGram
[input] configs/config

27. Recognition
[output] dic/tag_Net

28. Recognition
HVite –C configs/config -S scripts/test.scp –H monoHmms/m5/hmmdefs
-H Hmms/m7/macros –w dic/tag_Net –i mlfs/recOutWordm7.mlf
Dic/dict modelList/monoList

29. Recognition
[input] scripts/test_d.scp
[input] configs/config

30. Recognition
[input] modellist/wordList
[input]dic/dict

31. Recognition
[output] mlfs/pbw2_dependent_result.mlf

32. Recognition Analysis of Recognition Results
HResults –I mlfs/words.mlf modelList/wordList mlfs/pbw2_dependent_result.mlf