Diffusion language models and a CTC-USDM joint decoder improve ASR accuracy over standard approaches.
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Encoder-dominated ASR models using text-only data via modality matching and downsampling achieve comparable performance to larger-decoder models on LibriSpeech, with simple random duration approaches proving effective.
Tight integration of acoustic models with LLMs for ASR is ablated against shallow fusion across label units, fine-tuning strategies, LLM sizes, and joint CTC decoding to mitigate hallucinations.
citing papers explorer
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Diffusion Language Models for Speech Recognition
Diffusion language models and a CTC-USDM joint decoder improve ASR accuracy over standard approaches.
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Text-Utilization for Encoder-dominated Speech Recognition Models
Encoder-dominated ASR models using text-only data via modality matching and downsampling achieve comparable performance to larger-decoder models on LibriSpeech, with simple random duration approaches proving effective.
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LLMs and Speech: Integration vs. Combination
Tight integration of acoustic models with LLMs for ASR is ablated against shallow fusion across label units, fine-tuning strategies, LLM sizes, and joint CTC decoding to mitigate hallucinations.