Finite state and Constraint Grammar based Text-to-Speech processing
View the project on GitHub giellalt/speech-sme
html marking as clues
make a reverse speech recognition
The Antti model for now:
As comparision: the Trondheim model:
3Pekkakin sen möi
Minä puhun 2Pekastakin
cg-stype rule set:
word level
funct w deacc
pron v weak
num adv N adj strong
NP cost can be considered having focus and background
theme rheme
verb ....
if theme has two constituents
the first is sprominent
and others are secontary or olc
3Pekka 2kalan 1osta
3-keskiviikkona 2-kaikki tutkijat 1-læhtivæt kotiin
rheme
S O systemic ordering
deviations from syystemic ordering has prosodic contecequence
if A B in text
sys B A
act A B
=> prominent-B, less-prominent-A
hypothesis for finnish SO
subj < obj < loc < manner < osurce < goal
if any of these is moved to the left in the string
it will be part of the theme and therefor less prominent.
pekka osti KALAN-obj TORILTA-src
Pekka osti torilta-src KALAN-obj
rule against data driven approch: