1. Automatic identification of vocalic intervals in speech signal Jesus Garcia Antonio Galves Flaviane Fernandes Janaisa Viscardi Ulrike Gut Phonological Support: Many thanks to: Dafydd Gibbon Emmanuel Dupoux Franck Ramus

2. Problem We have: and we want: 0.179 0.301 v 0.301 0.390 c 0.390 0.440 v 0.440 0.498 c 0.498....

3. Identification Cues to the segmentation: " acoustic signal plot " spectrogram plot " listening

4. Acoustic Signal Spectrogram Noise Vowel Cons. Vowel. “A autoridade do governador diminuiu” Example:

5. Marca Noise Vowel Cons. Vowel.

6. vowels are recurrent

7. “t” signal:

8. c(i,t) = energy for the frequency i in time t

9. Relative entropy of the column t respect to the column s.

10. we use h(t-1,t), h(t-2,t) e h(t-3,t). plot for h(t): h(t)=h(t-1,t)+h(t-2,t) +h(t-3,t)

12. Distance between consecutives columns

19. Spectrogram for the sentence: ' A hurricane was announced this afternoon on the TV.'

20. Signal Relative Entropy 800-3000 hz Relative Entropy 0-800 hz

21. Spectrogram and segmentation for ' A hurricane was announced this afternoon on the TV.'

23. Box Plots for the integral of the relative entropy for each language

24. Spectrogram and R. entropy for the sentence with the lower integral

25. Ramus classification (1999). %V = vowel percent DeltaC = standard deviation of consonantal intervals