Modeling the intensity of syllables using classification and Regression Trees

The quality of the synthesized speech of text-to-speech (TTS) synthesis systems can be improved by controlling the intensities of speech segments in addition to other prosodic features such as intonation and duration. In this paper we proposed Classification and Regression Tree (CART) to model the intensities of syllables. Positional, contextual and phonological features associated to syllables are proposed to model the intensities. The proposed CART model is evaluated by means of objective measures such as average prediction error (μ), standard deviation (σ), correlation coefficient (γX,Y) and the percentage of syllables predicted within different deviations. From the studies we find that 82% of the syllable intensities could be predicted from the models within 7% deviation. The contribution of individual features in modeling the syllable intensities is also analysed. The proposed model is also evaluated by means of subjective listening tests on the synthesized speech generated by incorporating the predicted syllable intensities.