arXiv:2607.00946v1 Announce Type: cross Abstract: While prior work has explored emotion control in hybrid text-to-speech systems, the geometric properties of these modules, and their implications for steerability, remain poorly understood. We present the first comparative study of speech language model (SLM) and conditional flow-matching (CFM) modules as activation steering sites for mixed emotion speech synthesis. We first characterize emotion representations using linear probing and local intrinsic dimensionality (LID), and then evaluate single-site and joint steering for mixed-emotion synth
This research is published as AI models for speech synthesis become increasingly sophisticated, highlighting the ongoing effort to achieve nuanced and controllable emotional expression.
Advanced emotion steering in text-to-speech could lead to more engaging and human-like AI interactions, impacting various applications from customer service to entertainment.
The understanding of how to geometrically control and blend emotions in synthetic speech advances, potentially enabling more precise and composable emotional outputs.
- · AI developers
- · Creative industries
- · Customer service platforms
- · Legacy text-to-speech providers
More naturalistic and emotionally resonant AI-generated speech becomes achievable.
The development of highly personalized and emotionally adaptive AI interfaces accelerates.
The blurring of lines between human and synthetic communication deepens, raising new ethical considerations.
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Read at arXiv cs.LG