Diffusion-based TTS has demonstrated strong capability in emotional speech generation. However, existing emotion alignment methods primarily reinforce target emotional expressions while overlooking modeling of competing emotional modes, limiting fine-grained controllability. This issue becomes more pronounced under classifier-free guidance (CFG), which relies on contrasting conditional trajectories to shape generation. We propose Emotion Bidirectional Preference Optimization (Emo-BPO), a simple effective framework that jointly learns emotion-aligned and emotion-contrastive score functions from reordered same-text emotional pairs. Our method requires neither auxiliary reward models nor additional annotations, and can be seamlessly integrated into existing diffusion-based TTS systems to improve emotional controllability and expressiveness.
Figures & tables
Models
Emo SIM
Prosody SIM
Intelligibility
UTMOS
Speech Emotion Recognition
Neutral
Angry
Happy
Sad
Surprise
Mean
emospeech [ 8 ]
96.35
3.39
7.13
4.24
0.71
0.64
0.58
0.60
0.52
0.61
cosyvoice [ 10 ]
97.07
3.64
4.32
4.41
0.69
0.76
0.61
0.74
0.65
0.69
cosyvoice2 [ 11 ]
98.47
3.78
3.77
4.43
0.88
0.87
0.72
0.83
0.78
0.82
Emosphere++ [ 21 ]
98.16
3.69
4.94
4.35
0.79
0.81
0.67
0.77
0.69
0.75
EmoVoice [ 22 ]
98.59
3.67
4.16
4.39
0.87
0.85
0.76
0.80
0.79
0.81
Table 1: Objective evaluation: Comparison of Emo-BPO with representative emotional TTS baselines in terms of emotion similarity (Emo SIM), prosody similarity (Prosody SIM), intelligibility, naturalness (UTMOS), and speech emotion recognition accuracy. Emo-BPO achieves the best performance on Emo SIM, Prosody SIM, UTMOS, and mean emotion recognition accuracy while maintaining competitive intelligibility. Results are averaged over three independent runs.
Table 2
Figure 1: Overview of Emo-BPO. Training constructs inverted same-text emotional pairs to jointly learn emotion-aligned (EA) and emotion-contrastive (EC) diffusion branches while partially fine-tuning a pretrained backbone. At inference, EA and EC predictions are combined via contrastive guidance under classifier-free guidance, reinforcing target emotional trajectories and suppressing competing emotional modes.