On the perceptual analysis of intonation
Speech Communication
Analysis and synthesis of German F0 contours by means of Fujisaki's model
Speech Communication - Special issue: Fujisaki's Festschrift
Intelligibility of normal speech I: global and fine-grained acoustic-phonetic talker characteristics
Speech Communication - Special issue on acoustic echo control and speech enhancement techniques
Generating prosodic attitudes in French: data, model and evaluation
Speech Communication
Prosody modeling with soft templates
Speech Communication
Data-driven generation of F0 contours using a superpositional model
Speech Communication
A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis
Computer Speech and Language
Representation of Random Waveforms by Relational Trees
IEEE Transactions on Computers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a novel prosody model in the context of computer text-to-speech synthesis applications for tone languages. We have demonstrated its applicability using the Standard Yoruba (SY) language. Our approach is motivated by the theory that abstract and realised forms of various prosody dimensions should be modelled within a modular and unified framework [Coleman, J.S., 1994. Polysyllabic words in the YorkTalk synthesis system. In: Keating, P.A. (Ed.), Phonological Structure and Forms: Papers in Laboratory Phonology III, Cambridge University Press, Cambridge, pp. 293-324]. We have implemented this framework using the Relational Tree (R-Tree) technique. R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. The underlying assumption of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combine acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. To implement the intonation dimension, fuzzy logic based rules were developed using speech data from native speakers of Yoruba. The Fuzzy Decision Tree (FDT) and the Classification and Regression Tree (CART) techniques were tested in modelling the duration dimension. For practical reasons, we have selected the FDT for implementing the duration dimension of our prosody model. To establish the effectiveness of our prosody model, we have also developed a Stem-ML prosody model for SY. We have performed both quantitative and qualitative evaluations on our implemented prosody models. The results suggest that, although the R-Tree model does not predict the numerical speech prosody data as accurately as the Stem-ML model, it produces synthetic speech prosody with better intelligibility and naturalness. The R-Tree model is particularly suitable for speech prosody modelling for languages with limited language resources and expertise, e.g. African languages. Furthermore, the R-Tree model is easy to implement, interpret and analyse.