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Segmental duration modelling in Turkish
Date
2006-01-01
Author
Ozturk, Ozlem
Çiloğlu, Tolga
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Naturalness of synthetic speech highly depends on appropriate modelling of prosodic aspects. Mostly, three prosody components are modelled: segmental duration, pitch contour and intensity. In this study, we present our work on modelling segmental duration in Turkish using machine-learning algorithms, especially Classification and Regression Trees. The models predict phone durations based on attributes such as current, preceding and following phones' identities, stress, part-of-speech, word length in number of syllables, and position of word in utterance extracted from a speech corpus. Obtained models predict segment durations better than mean duration approximations (similar to 0.77 Correlation Coefficient, and 20.4 ms Root-Mean Squared Error). In order to improve prediction performance further, attributes used to develop segmental duration are optimized by means of Sequential Forward Selection method. As a result of Sequential Forward Selection method, phone identity, neighboring phone identities, lexical stress, syllable type, part-of-speech, phrase break information, and location of word in the phrase constitute optimum attribute set for phoneme duration modelling.
Subject Keywords
Mean absolute error
,
Pitch contour
,
Synthetic speech
,
Speech corpus
,
Speech database
URI
https://hdl.handle.net/11511/54581
Journal
TEXT, SPEECH AND DIALOGUE, PROCEEDINGS
Collections
Department of Electrical and Electronics Engineering, Article