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Post 37
FALA 2010 conference
15-Sep-2010
Good news. Our work on text classification of domain-styled text and sentiment-styled text for
expressive speech synthesis
has been selected for presentation at the
FALA 2010 conference.
The conference will be held on November at Vigo, Spain.
In the context of text processing for Text-to-Speech (TTS) synthesis,
we aim to automatically direct the expressiveness in
speech through tagging the input text appropriately. Since
the nature of text presents different characteristics according
to whether it is domain-dependent (expressiveness related to
its topics) or sentiment-dependent (expressiveness related to its
sentiment), we study how these traits influence the identification
of expressiveness in text, and develop a successful classification
strategy.
To this end, we consider two principal Text Classification
(TC) methods, the Reduced Associative Relational Network
and the Maximum Entropy classifier, and evaluate their performed
effectiveness in domain/sentiment dependent environments.
Additionally, we also evaluate how sensitive the classifiers
are to the size of training data. The overall conclusions indicate
that moving from a domain-dependent environment to a
more general sentiment-dependent environment strictly results
in poorer effectiveness rates, despite the sensible generalisation
advantage that sentiment provides for dealing with expressiveness.
There is also little influence on the size of the training
data.
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