Letter to Phoneme Alignment

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

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

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

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

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 L2P aligner Aligned Dictionary Dictionary Brick brIk Brightening br2tHIN British brItIS Bronx brQNks Bugle bjugP Buoy 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|

7. Evaluation • No Aligned Dictionary • Unsupervised Learning • Previously aligner was tied with a generator • Evaluation on percentage of correctly predicted phonemes and words Tee’s L2P Generator Aligned Dictionary Accuracy

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

9. Static Model, Structure • Independent sub alignments l1 l2 l3 l4 ln-1 ln a1 a2 ak p1 p2 p3 p4 pm-1 pm

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

11. Result of Static Model

12. Dynamic Model • Sequence of data • Unrolled model for T=3 slices l1 l2 l3 l4 l5 l6 a1 a2 ak p1 p2 p3 p4 p5 p6

13. Questions