TargetSEC: Plug-and-Play In-the-Wild Speech Emotion Conversion via Arousal-Conditioned Latent Style Diffusion
arXiv:2606.07293v1 Announce Type: cross Abstract: Speech Emotion Conversion (SEC) aims to transform the emotion of a source utterance into a target emotion while preserving content and speaker identity. SEC on in-the-wild data is challenging due to the non-parallel nature of training data and complex real-world acoustics. Existing fixed-duration approaches either struggle to shift the emotion effectively (high quality, low conversion) or degrade speech naturalness (low quality, high conversion). We propose TargetSEC, an embedding-driven latent diffusion framework that generates emotion-focused
The proliferation of advanced AI models and diffusion architectures is enabling more nuanced control over generated content, pushing research into highly specific and challenging applications like in-the-wild speech emotion conversion.
Improving speech emotion conversion in unconstrained environments opens new avenues for AI in mental health, human-computer interaction, and content creation, making AI-generated speech more emotionally resonant and natural.
This research enhances the ability of AI systems to manipulate emotional expression in speech without degrading quality or losing speaker identity, moving closer to realistic and controllable emotional synthesis.
- · AI voice synthesis companies
- · Mental health tech platforms
- · Content creators (e.g., gaming, film)
- · Human-computer interaction developers
- · Platforms reliant on static, emotionless AI voices
- · Traditional voice acting for specific emotional modulation
More sophisticated and emotionally expressive AI assistants and conversational agents will emerge.
The ethical implications of easily manipulable emotional speech will become a more pressing concern, requiring new detection and regulation technologies.
Personalized therapeutic applications using AI to model and adapt emotional responses in speech could become a reality, impacting treatment for speech or emotional disorders.
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Read at arXiv cs.LG