Adaptation of prosodic phrasing models
Peter Bell, Tina Burrows, Paul R.P. Taylor · 2006
There is considerable variation in the prosodic phrasing of speech between different speakers and speech styles.Due to the time and cost of obtaining large quantities of data to train a model for every variation, it is desirable to develop models that can be adapted to new conditions with a limited amount of training data.We describe a technique for adapting HMMbased phrase boundary prediction models which alters a statistical distribution of prosodic phrase lengths.The adapted models show improved prediction performance across different speakers and types of spoken material.