letter to phoneme alignment

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Letter to Phoneme Alignment. Using Graphical Models. N. Bolandzadeh, R. Rabbany Dept of Computing Science University of Alberta. 1. Text to Speech Problem. Conversion of Text to Speech: TTS Automated Telecom Services E-mail by Phone Banking Systems Handicapped People. Pronunciation. - PowerPoint PPT Presentation

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Letter to Phoneme Letter to Phoneme AlignmentAlignment

Using Graphical Models

N. Bolandzadeh, R. Rabbany

Dept of Computing ScienceUniversity of Alberta

11

Text to Speech Text to Speech ProblemProblem

Conversion of Text to Speech: TTS

◦Automated Telecom Services◦E-mail by Phone◦Banking Systems◦Handicapped People

2

PronunciationPronunciation

Pronunciation of the words Dictionary Words Non-Dictionary Words

Phonetic analysis Dictionary lookup?

Language is alive, new words addProper Nouns

Machine Learning higher accuracyL 2 P alignment is needed

3

4

ProblemProblemLetter to Phoneme Alignment

◦ Letter: c a k e

◦ Phoneme: k ei k

4

L2P

Automatic Speech Recognition

&

Spelling Correction

5

It's not Trivial! It's not Trivial! why?why?

No Consistency◦City / s /◦Cake / k /◦Kid / k /

No Transparency◦K i d (3) / k i d / (3) ◦S i x (3) / s i k s / (4)◦Q u e u e (5) / k j u: / (3)◦A x e (3) / a k s / (3)

5

FrameworkFramework

6

Brick brIkBrightening br2tHINBritishbrItISBronx brQNksBugle bjugPBuoy b4

b|r|i|ck| b|r|I|k|b|r|ig|ht|en|i|ng| b|r|2|t|H|I|N|b|r|i|t|i|sh| b|r|I|t|I|S|b|r|o|n|x| b|r|Q|N|ks|b|u|g|le| b|ju|g|P|bu|oy| b|4|

EvaluationEvaluationNo Aligned DictionaryUnsupervised LearningPreviously aligner was tied with a

generator

Evaluation on percentage of correctly predicted phonemes and words

7

Model of our problemModel of our problem

8

mn pppPlllL ...... 2121

2|||,|

,

...

),|(maxarg

21

ii

iii

k

Abest

PL

PLa

aaaA

PLAPA

B | r | i | t | i | sh |B | r | I | t | I | S |

Static Model, StructureStatic Model, StructureIndependent sub alignments

9

l1 l2

p1 p2

a1

k

iiii PLaPAP

1

),|()(

l3 l4

p3 p4

a2

ln-1 ln

pm-1

pm

ak

Static Model, LearningStatic Model, LearningEM

◦Initialize Parameters◦Expectation Step:

Parameters Alignments

◦Maximization Step: Alignments Parameters

10

Result of Static ModelResult of Static Model

11

Method Letters Words

Static Model

81.34% 43.5%

Dynamic ModelDynamic Model

12

Sequence of dataUnrolled model for T=3 slices

l1 l2

p1 p2

a1

l3 l4

p3 p4

a2

l5 l6

p5 p6

ak

QuestionsQuestions

13

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