Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
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
(2) Helsinki University of Technology, Acoustics Laboratory, Finland
(3) University of Joensuu, Department of Linguistics, 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.


Full Paper

Bibliographic reference.  Vainio, Martti / Altosaar, Toomas / Werner, Stefan (2000): "Measuring the importance of morphological information for finnish speech synthesis", In ICSLP-2000, vol.1, 641-644.