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

Collections

Department of Electrical and Electronics Engineering, Article