On synthetic banking speech alone, GRPO cuts ASR WER 40% relative to SFT (36.71%→22.09%) by improving stopping calibration and attention anchoring to audio.
Text-only adaptation in llm- based asr through text denoising
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Layer-wise pooling plus RIR-augmented synthetic speech matches a 100%-real ASR baseline with only 25% real data (13.6 h) and beats it at higher real fractions.
CORTIS is a text-only adaptation method for spoken language models that enables direct speech-to-structured-output generation for task-oriented agents and matches or exceeds ASR-LLM cascades under acoustic degradation.
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.
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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When Synthetic Speech Is All You Have: Better Call GRPO
On synthetic banking speech alone, GRPO cuts ASR WER 40% relative to SFT (36.71%→22.09%) by improving stopping calibration and attention anchoring to audio.
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How to Leverage Synthetic Speech for LLM-Based ASR Systems?
Layer-wise pooling plus RIR-augmented synthetic speech matches a 100%-real ASR baseline with only 25% real data (13.6 h) and beats it at higher real fractions.
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CORTIS: Text-Only Adaptation of Spoken Language Models for Task-Oriented Voice Agents
CORTIS is a text-only adaptation method for spoken language models that enables direct speech-to-structured-output generation for task-oriented agents and matches or exceeds ASR-LLM cascades under acoustic degradation.
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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.