arXiv:2606.01479v1 Announce Type: new Abstract: Integrating large language models (LLMs) into text-to-speech (TTS) systems has improved speech expressiveness, yet interpretable emotional control remains challenging. Existing approaches primarily rely on external conditioning or global activation steering, offering limited insight into the internal representations underlying emotional control. In this work, we analyze emotion-related variation in the semantic hidden states of LLM-based TTS models using sparse autoencoders (SAEs) to identify sparse latent features. Our analysis shows that emotio
The increasing integration of LLMs into TTS systems highlights the current challenges in achieving interpretable emotional control, prompting new research into methods like sparse autoencoders.
This development improves control and understanding of emotional expression in AI-generated speech, critical for more natural human-computer interaction and advanced AI applications.
Researchers can now better identify and manipulate specific latent features responsible for emotional variation in text-to-speech models, moving beyond less interpretable methods.
- · AI developers
- · Text-to-speech companies
- · Generative AI platforms
- · Platforms lacking fine-grained emotional control in AI speech
More expressive and nuanced AI-generated voices become achievable for various applications.
Improved emotional AI could lead to more engaging and personalized user experiences across interfaces.
The ability to precisely control emotion in AI speech may raise ethical considerations regarding manipulation or synthetic empathy.
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Read at arXiv cs.CL