  Recent advances in text-to-speech, particularly those based on Graph Neural
Networks (GNNs), have significantly improved the expressiveness of short-form
synthetic speech. However, generating human-parity long-form speech with high
dynamic prosodic variations is still challenging. To address this problem, we
expand the capabilities of GNNs with a hierarchical prosody modeling approach,
named HiGNN-TTS. Specifically, we add a virtual global node in the graph to
strengthen the interconnection of word nodes and introduce a contextual
attention mechanism to broaden the prosody modeling scope of GNNs from
intra-sentence to inter-sentence. Additionally, we perform hierarchical
supervision from acoustic prosody on each node of the graph to capture the
prosodic variations with a high dynamic range. Ablation studies show the
effectiveness of HiGNN-TTS in learning hierarchical prosody. Both objective and
subjective evaluations demonstrate that HiGNN-TTS significantly improves the
naturalness and expressiveness of long-form synthetic speech.
