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AURA is proposed, an ultra-efficient representation-editing method that freezes the pretrained model and applies sparse scale-and-shift edits to decoder cross-attention heads, supporting dynamic activation editing as a practical path for grounding AED speech models.
Encoder Awakening via Adapters (EAVA), a simple yet effective domain-adaptive fine-tuning method for Speech-LLM-based ASR, consistently outperforms vanilla fine-tuning and other baselines, achieving new state-of-the-art performance.
This work presents the first black-box MIA framework explicitly tailored to TTS models at both the speaker and record levels, and characterize the feasible query space and establish two criteria, scorable extent and memorization elicitation, for evaluating five representative queries.
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