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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