Voice-cloning TTS models learn interpretable acoustic concepts, but the relationship between understanding what a model knows and controlling it is complex—strong probing doesn't guarantee effective steering, and different feature extraction methods work better for different tasks.
This paper investigates what acoustic features voice-cloning TTS models learn and use by applying interpretability techniques like sparse autoencoders, PCA, and probing to Qwen3-TTS and five other models.