Sixth International Conference on Spoken Language Processing
October 16-20, 2000
Measuring the Importance of Morphological Information for Finnish Speech Synthesis
Martti Vainio (1), Toomas Altosaar (2), Stefan Werner (3)
(1) Department of Phonetics, University of Helsinki, Finland
The basic assumption in intonation models and perhaps generally
in prosody models is, that part-of-speech information is
of paramount importance for predicting the actual values for the
prosodic parameters; be they pitch, segmental duration or loudness.
We have studied whether morphological knowledge, in addition
to part-of-speech and functional information, is of any help
in predicting prosody in a morphologically rich language such as
Finnish. Our research concerns Finnish prosody with respect to
pitch and segmental duration. The basic methodology we employ
is based on artificial neural networks. It is a continuation of our
earlier studies on prosody where we investigated the problem of
generating values for prosodic parameters for text-to-speech synthesis.
The basic methodology we employ is based on standard
multi-layer feed-forward networks that are trained with backpropagation.
The results we have obtained show that there are certain
advantages in adding morphological knowledge to the network
input. Apart from part-of-speech information, there are certain
cases where morphological features seem to affect the outcome
of both pitch and segmental durations. This behavior can be expected
in a morphologically rich language.
(2) Helsinki University of Technology, Acoustics Laboratory, Finland
(3) University of Joensuu, Department of Linguistics, Finland
Vainio, Martti / Altosaar, Toomas / Werner, Stefan (2000):
"Measuring the importance of morphological information for finnish speech synthesis",
In ICSLP-2000, vol.1, 641-644.