# HiP-ETTS

Multimodal emotional text-to-speech previously packed cross-speaker emotion regularities and individual expression into one emotion condition, so strengthening emotion could damage linguistic content. HiP-ETTS splits prompt-derived emotion into an emotion-shared factor and a complementary emotion factor, anchors linguistic structure with a hierarchical emotional prior, and refines emotional detail through adaptive projection and flow matching. Across audio, text and facial-image prompts its word error rate is lower than or comparable to prior methods, and its VAD-CCC and Emo-SIM emotion-consistency scores are the highest in every prompt condition, reaching 0.760 and 0.768 under audio prompts.

Multimodal emotional text-to-speech previously packed cross-speaker emotion regularities and individual expression into one emotion condition, so strengthening emotion could damage linguistic content. HiP-ETTS splits prompt-derived emotion into an emotion-shared factor and a complementary emotion factor, anchors linguistic structure with a hierarchical emotional prior, and refines emotional detail through adaptive projection and flow matching. Across audio, text and facial-image prompts its word error rate is lower than or comparable to prior methods, and its VAD-CCC and Emo-SIM emotion-consistency scores are the highest in every prompt condition, reaching 0.760 and 0.768 under audio prompts.