Segmental Duration Modelling in Turkish

  • Özlem Öztürk
  • Tolga Çiloğlu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4188)


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 (~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.


Mean Absolute Error Pitch Contour Synthetic Speech Speech Corpus Speech Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Özlem Öztürk
    • 1
  • Tolga Çiloğlu
    • 2
  1. 1.Electrical and Electronics Engineering DepartmentDokuz Eylul UniversityIzmirTurkey
  2. 2.Electrical and Electronics Engineering DepartmentMiddle East Technical UniversityAnkaraTurkey

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