The assignment of prosodic events (accent and phrasing) from the text is crucial in text-to-speech synthesis systems. This paper addresses the combination of linguistic and metric constraints for the assignment of prosodic events in text-to-speech synthesis. First, a linguistic processing chain is used to provide a rich linguistic description of a text. Then, a novel statistical representation based on a hierarchical HMM (HHMM) is used to model the prosodic structure of a text: the root layer represents the text, each intermediate layer a sequence of intermediate phrases, the pre-terminal layer the sequence of accents, and the terminal layer the sequence of linguistic contexts. For each intermediate layer, a segmental HMM and information fusion are used to fuse the linguistic and metric constraints for the segmentation of a text into phrases. A set of experiments conducted on multi-speaker databases with various speaking styles reports that: the rich linguistic representation improves drastically the assignment of prosodic events, and the fusion of linguistic and metric constraints significantly improves over standard methods for the segmentation of a text into phrases. These constitute substantial advances that can be further used to model the speech prosody of a speaker, a speaking style, and emotions for text-to-speech synthesis
