Unsupervised Segmentation of Audio Speech using the Voting Experts Algorithm

1. Unsupervised Segmentation of Audio Speech using the Voting Experts Algorithm Matthew Miller, Alexander Stoytchev Developmental Robotics Lab Department of Electrical and Computer Engineering Iowa State University mamille@cs.iastate.edu, alexs@iastate.edu www.cs.iastate.edu/~mamille/

2. Language: A Grand Challenge • A working example • Automatically acquires language • Well studied

3. Statistical Learning Experiments Saffran et. al. (1996): 8-month-olds can segment speech. Artificial Language: Acclimate tupiro golabu bedaku padoti Language: tu pi ro go la bu be da ku Transition Prob: 1.0 1.0 .25 1.0 1.0 .25 1.0 1.0 ... NovelWord • Hypothesis: Infants use local minima in single syllable transition probabilities to segment speech streams.

4. Voting Experts An algorithm for unsupervised segmentation Key Idea: Natural “chunks” have: Low Internal Information High Boundary Entropy itwasabrightcolddayinaprilandtheclockswere

5. Voting Experts An algorithm for unsupervised segmentation Key Idea: Natural “chunks” have: Low Internal Information High Boundary Entropy itwasabrightcolddayinaprilandtheclockswere

6. VE Implementation (Cohen 2006) Build an n-gram trie from text. Slide a window along the text sequence Two experts vote how to break the window One minimizes internal info Other maximizes boundary entropy Window i t w a s a b r i g h t c o l d d a y i n a p r i l 1

7. VE Implementation (Cohen 2006) Build an n-gram trie from text. Slide a window along the text sequence Two experts vote how to break the window One minimizes internal info Other maximizes boundary entropy Window i t w a s a b r i g h t c o l d d a y i n a p r i l 2

8. VE Implementation (Cohen 2006) Build an n-gram trie from text. Slide a window along the text sequence Two experts vote how to break the window One minimizes internal info Other maximizes boundary entropy Break at vote peaks 1 1 0 0 6 1 0 2 0 1 0 0 3 0 3 i | t | w | a | s | a | b | r | i | g | h | t | c | o | l | d i t w a s a b r i g h t c o l d d a y i n a p r i l

9. VE Results • Results are surprisingly good on text • Especially giving its simplicity • Accuracy and Hit rate about 75% • Seems to capture something about the nature of “chunks” • Can we use this algorithm to segment real audio? It was a br igh t

10. Acoustic Model

11. Acoustic Model • Cluster spectral features using a GGSOM

12. Acoustic Model • Cluster spectral features using a GGSOM • Collapse state sequence

13. Acoustic Model • Cluster spectral features using a GGSOM • Collapse state sequence • Run VE to get breaks

14. Experiments and Results • Used the model to segment “1984” • CD 1 of audio book (40 mins) • Chosen for length, consistency • Evaluation: Human graders

15. New Experiments • Trained on infant datasets • Tested on manually generated keys Train Train Stream A: Acoustic Model A VE Model A Key A tupiro golabu bedaku padoti Test Test Test Test Stream B: Acoustic Model B VE Model B Key B dapiku tilado pagotu burobi Train Train

16. New Experiments • Trained on infant datasets • Tested on manually generated keys Stream A: Acoustic Model A VE Model A Key B tupiro golabu bedaku padoti Test Test Test Test Stream B: Acoustic Model B VE Model B Key A dapiku tilado pagotu burobi

17. Results • Experiment 1 • Accuracy: 50% on all induced breaks • Hit Rate: 75% of word breaks • Significantly better than chance • Experiment 2 • Accuracy: 16% on all induced breaks • Hit Rate: 1% of word breaks • Worse than chance • 18 breaks, 3 correct

18. Conclusions and Future Work • VE Model can be used to segment audio • Can reproduce the results of Infant studies • May model part of the human chunking mechanism • Have built more sophisticated acoustic models • Better results (nearly perfect)

19. Thank You • www.cs.iastate.edu/~mamille/