1. Letter to Phoneme Alignment Using Graphical Models N. Bolandzadeh, R. Rabbany Dept of Computing Science University of Alberta 1 1

2. Text to Speech Problem Conversion of Text to Speech: TTS ◦ Automated Telecom Services ◦ E-mail by Phone ◦ Banking Systems ◦ Handicapped People 2

3. Pronunciation Pronunciation of the words  Dictionary Words  Non-Dictionary Words Phonetic analysis Dictionary lookup? Language is alive, new words add Proper Nouns Machine Learning  higher accuracy  L 2 P alignment is needed 3

4. 4 Problem Letter to Phoneme Alignment ◦ Letter : c a k e ◦ Phoneme : k ei k  4 L2P Automatic Speech Recognition & Spelling Correction

5. 5 It's not Trivial! 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

6. Framework 6 BrickbrIk Brighteningbr2tHIN BritishbrItIS BronxbrQNks BuglebjugP Buoyb4 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|

7. Evaluation No Aligned Dictionary Unsupervised Learning Previously aligner was tied with a generator Evaluation on percentage of correctly predicted phonemes and words 7

8. Model of our problem 8 B | r | i | t | i | sh | B | r | I | t | I | S |

9. Static Model, Structure Independent sub alignments 9 l1l1 l1l1 l2l2 l2l2 p1p1 p1p1 p2p2 p2p2 a1a1 l3l3 l3l3 l4l4 l4l4 p3p3 p3p3 p4p4 p4p4 a2a2 l n-1 lnln lnln p m-1 pmpm pmpm akak

10. Static Model, Learning EM ◦ Initialize Parameters ◦ Expectation Step:  Parameters  Alignments ◦ Maximization Step:  Alignments  Parameters 10

11. Result of Static Model 11 MethodLettersWords Static Model81.34%43.5%

12. Dynamic Model 12 Sequence of data Unrolled model for T=3 slices l1l1 l1l1 l2l2 l2l2 p1p1 p1p1 p2p2 p2p2 a1a1 l3l3 l3l3 l4l4 l4l4 p3p3 p3p3 p4p4 p4p4 a2a2 l5l5 l5l5 l6l6 l6l6 p5p5 p5p5 p6p6 p6p6 akak

13. Questions 13