This paper presents an intonation generation system for use in a text-to-speech synthesis system. The intonation generation system uses classification trees to predict intonation event location and regression trees to predict parameters relating to the F0 shape for the predicted events. The decision trees model intonation within the Tilt intonation model, which provides a parameterized description of fundmaental frequency and an intuitive labelling scheme. The event location trees predict an event class (e.g. accent, boundary, none) for each syllable in an utterance based on local and global context (e.g. stress, phrasing, part of speech). The parameter prediction trees then provide the parameterized description of each intonation event based on similar context features. Informal results of the full system are presented together with results for the individual components.
This paper describes the development and evaluation of objective methods for testing synthetic intonation. While subjective methods are available for assessing the quality of synthetic intonation, such tests consume time and resources, and are not useful for day-to-day model development. Therefore, objective measures of F0 modelling are necessary. Currently, objective evaluation of synthetic intonation involves the use of Root Mean Squared Error and Correlation. However, it is unclear how large an improvement in either score must be before it is reflected perceptually. It is also unclear how detailed an analysis these metrics provide. Therefore, two other metrics are to be tested, both of which are similar to a basic RMSE measurement. All of the evaluation results are compared to a perceptual study in order to determine how the objective measures relate to perceived differences in the contours.
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