Papers with generative error correction
Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses (2026.findings-acl)
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| Challenge: | Recent advances have introduced GER frameworks that utilize LLMs to refine ASR outputs. |
| Approach: | They propose a framework that allows a large language model to compose independent N-best hypotheses from separate automatic speech recognition (ASR) and visual speech recognition models. |
| Outcome: | The proposed framework achieves 57.7% error rate gain over standard ASR baseline, compared to single-stream approaches that achieve only 10% gain. |
CoVoGER: A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models can fix recognition or translation errors that traditional rescoring cannot fix. |
| Approach: | They propose a benchmark for GER that covers both ASR and speech-to-text translation across 15 languages and 28 language pairs. |
| Outcome: | The proposed benchmark is built on common voice 20.0 and CoVoST-2 with Whisper and SeamlessM4T. |
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition (2023.emnlp-main)
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Srijith Radhakrishnan, Chao-Han Yang, Sumeer Khan, Rohit Kumar, Narsis Kiani, David Gomez-Cabrero, Jesper Tegnér
| Challenge: | Existing methods for generative error correction in automatic speech recognition (ASR) use a two-pass reranking paradigm to generate n-best hypotheses. |
| Approach: | They propose a cross-modal fusion technique for generative error correction in automatic speech recognition. |
| Outcome: | The proposed technique shows a 37.66% improvement in word error rate relative to the n-best Oracle. |
Generative Error Correction for Emotion-aware Speech-to-text Translation (2025.findings-acl)
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| Challenge: | Despite recent advances in speech-to-text translation, the impact of the emotion content has been overlooked. |
| Approach: | They propose to use generative error correction (GER) to generate the translation based on the decoded N-best hypotheses and combine emotion and sentiment labels into the LLM finetuning process to enable the model to consider the emotion content. |
| Outcome: | The proposed model can translate speech in English-Chinese using GER and emotion and sentiment labels. |