Automatic identification of vocalic intervals in speech signal

Jesus Garcia Antonio Galves

Flaviane FernandesJanaisa ViscardiUlrike Gut

Phonological Support:

Many thanks to:

Dafydd GibbonEmmanuel DupouxFranck Ramus

ProblemWe have:

and we want:

0.179 0.301 v 0.301 0.390 c 0.390 0.440 v0.440 0.498 c0.498 ....

Identification

Cues to the segmentation:

acoustic signal plotspectrogram plotlistening

Acoustic Signal

Spectrogram

Noise

Vowel

Cons.

Vowel.

“A autoridade do governador diminuiu”

Example:

Marca

Noise

Vowel

Cons.

Vowel.

vowels are recurrent

“t” signal:

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

pi,t c i,t k1

N

ck,t

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

h t,s p i,t ln p i,t p i,s

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)

Distance between consecutives columns

d t,t1 p i,t pi,t12

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

Signal

Relative Entropy800-3000 hz

Relative Entropy0-800 hz

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

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

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

Ramus classification (1999).

%V x Delta C

jap

catspa ita

fre

dutpol

eng

3

3,5

4

4,5

5

5,5

40 45 50 55

Del

taC

%V

%V = vowel percent

DeltaC = standard deviation of consonantal intervals