Automatic identification of vocalic intervals in speech signal Jesus Garcia Antonio Galves Flaviane...
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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
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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 ....
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Identification
Cues to the segmentation:
acoustic signal plotspectrogram plotlistening
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Acoustic Signal
Spectrogram
Noise
Vowel
Cons.
Vowel.
“A autoridade do governador diminuiu”
Example:
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Marca
Noise
Vowel
Cons.
Vowel.
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vowels are recurrent
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“t” signal:
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c(i,t) = energy for the frequency i in time t
pi,t c i,t k1
N
ck,t
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Relative entropy of the column t respect to the column s.
h t,s p i,t ln p i,t p i,s
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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)
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Distance between consecutives columns
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d t,t1 p i,t pi,t12
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Spectrogram for the sentence:' A hurricane was announced this afternoon on the TV.'
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Signal
Relative Entropy800-3000 hz
Relative Entropy0-800 hz
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Spectrogram and segmentation for' A hurricane was announced this afternoon on the TV.'
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Box Plots for the integral of the relative entropy for each language
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Spectrogram and R. entropy for the sentence with the lower integral
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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
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