Abstract: In this paper, we present a novel statistical approach to
corpus-based speech synthesis. Classically, phonetic information is
defined and considered as acoustic reference to be respected. In this
way, many studies were elaborated for acoustical unit classification.
This type of classification allows separating units according to their
symbolic characteristics. Indeed, target cost and concatenation cost
were classically defined for unit selection.
In Corpus-Based Speech Synthesis System, when using large text
corpora, cost functions were limited to a juxtaposition of symbolic
criteria and the acoustic information of units is not exploited in the
definition of the target cost.
In this manuscript, we token in our consideration the unit phonetic
information corresponding to acoustic information. This would be realized
by defining a probabilistic linguistic Bi-grams model basically
used for unit selection. The selected units would be extracted from
the English TIMIT corpora.