Modeling Intonation

We build our model on top of Stem-ML[8] because it captures several desirable properties. A positive feature of Stem-ML is that the representation is understandable, adjustable, and can be transported from one situation to another.

Unlike most approaches, this model cleanly separates into local (word-dependent) and global (speaker-dependent) parameters. For instance, one can generate acceptable speech by using the templates of one speaker with prosodic strengths from another[15], where a female speaker's tone templates were used as part of a model to predict a male speaker's f₀ contours. Unlike some descriptive models, we predict numerical f₀ values, and so our model is subject to quantitative test, and can be extended to testing linguistic theories. Few other approaches to intonation have these properties.