TASU2 adds controllability over uncertainty and error rate to text-derived CTC simulation, enabling better cross-modal alignment and low-resource adaptation for speech LLMs than prior text-only or TTS methods.
Text-only adaptation in llm- based asr through text denoising
2 Pith papers cite this work. Polarity classification is still indexing.
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Mixed batching with only 10% target-domain speech achieves word error rates matching or exceeding conventional full-dataset ASR fine-tuning in LLM-based models.
citing papers explorer
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TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs
TASU2 adds controllability over uncertainty and error rate to text-derived CTC simulation, enabling better cross-modal alignment and low-resource adaptation for speech LLMs than prior text-only or TTS methods.
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Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR
Mixed batching with only 10% target-domain speech achieves word error rates matching or exceeding conventional full-dataset ASR fine-tuning in LLM-based models.