# Emotional Text-to-Speech

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researchsquare.com Nov 3, 2026
Emotional TTS stops blending speaker style into emotion: HiP-ETTS splits prompts into shared and complementary factors and reaches 2.98% WER 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.
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.
University of Auckland
Ziyu Zheng（First author）
HiP-ETTS Emotional Text-to-Speech Flow Matching
arXiv Sep 30, 2026
EmoRES splits emotion vectors into shared and residual parts, lifting emotion hit rate by up to 12.95 points on IndexTTS-2 and CosyVoice2
The work proposes EmoRES, a training-free method that discovers that an emotion steering vector decomposes into a shared component moving speech away from neutral expression and a residual component directing generation toward the requested emotion, and controls the two independently; on IEMOCAP, EmoRES outperforms CoCoEmo across all four objective emotion metrics on the frozen IndexTTS-2 and CosyVoice2 backbones, improving rank correlation by 26.13 and 12.97 percentage points and emotion hit rate by 12.95 and 6.92 points, with human evaluation showing up to 35.0% relative improvement in listeners correctly identifying the dominant requested emotion and up to 63.8% naturalness preference in pairwise comparisons.
The work proposes EmoRES, a training-free method that discovers that an emotion steering vector decomposes into a shared component moving speech away from neutral expression and a residual component directing generation toward the requested emotion, and controls the two independently; on IEMOCAP, EmoRES outperforms CoCoEmo across all four objective emotion metrics on the frozen IndexTTS-2 and CosyVoice2 backbones, improving rank correlation by 26.13 and 12.97 percentage points and emotion hit rate by 12.95 and 6.92 points, with human evaluation showing up to 35.0% relative improvement in listeners correctly identifying the dominant requested emotion and up to 63.8% naturalness preference in pairwise comparisons.
National Taiwan University
Kuan-Po Huang（First author）
EmoRES (Emotion Residual-Enhanced Steering)Activation Steering Emotional Text-to-Speech

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Emotional TTS stops blending speaker style into emotion: HiP-ETTS splits prompts into shared and complementary factors and reaches 2.98% WER under audio prompts

EmoRES splits emotion vectors into shared and residual parts, lifting emotion hit rate by up to 12.95 points on IndexTTS-2 and CosyVoice2