Researchers have presented a novel study exploring the geometric properties of emotion control in text-to-speech (TTS) systems. The study compares speech language models (SLMs) and conditional flow-matching (CFM) modules as sites for steering mixed emotions in speech synthesis. Findings indicate that SLMs provide a distinct, low-dimensional subspace for emotions with good speaker-emotion disentanglement, whereas CFM modules show weaker cross-speaker performance due to entangled speaker and emotion representations. Joint steering can enhance emotion intensity but may reduce proportional control and speech quality. AI
IMPACT Provides insights for developing more controllable and nuanced emotional expression in speech synthesis systems.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new study on text-to-speech models. [lever_c_demoted from research: ic=1 ai=1.0]
- A Geometric Perspective on Composable Emotion Steering in Text-to-Speech Models
- alphaXiv
- arXiv
- CatalyzeX
- Conditional Flow Matching
- DagsHub
- Gotit.pub
- Hugging Face
- Local Intrinsic Dimensionality Based Features for Clustering
- ScienceCast
- Speech Language Model
- speech synthesis
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