arXiv:2606.10029v1 Announce Type: cross Abstract: Language models increasingly serve as the backbone of text-to-speech (TTS) systems, yet we understand little about the representations they build when text and generated speech tokens share a single residual stream. We train BatchTopK sparse autoencoders on the LM backbone of CosyVoice3 and introduce a modality-aware auto-interp pipeline that labels each feature from where it fires-text-prefix context, 1-second speech clips, or both. The recovered features are interpretable, spanning phonemes, laughter, accent prompts and speaker gender. Steeri
This paper represents a tangible step in understanding and controlling the internal workings of complex AI models, particularly in the rapidly advancing field of text-to-speech technology, which is seeing continuous innovation.
A strategic reader should care because improved interpretability and steerability of large language models for TTS can lead to more reliable, controllable, and ethically sound AI applications, pushing the boundaries of human-computer interaction.
The ability to identify and manipulate specific features like phonemes, laughter, accents, and gender within a TTS model's residual stream marks a significant advance in granular control over AI output.
- · AI developers
- · Creative industries relying on AI-generated speech
- · Researchers in AI safety and interpretability
- · Users of TTS technologies
- · Black-box AI models
- · Approaches lacking interpretability
- · Malicious actors aiming for undetectable AI audio manipulation
Text-to-speech systems become more customizable and less prone to generating unintended or biased outputs.
This methodology could be adapted to other multimodal AI systems, enhancing control and understanding across diverse applications.
Enhanced interpretability could accelerate responsible AI development and deployment, potentially influencing regulatory frameworks for AI systems.
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