An LLM-based ASR error corrector trained on synthetic rare-word speech and given simplified phonetic context lowers WER/CER and raises rare-word recall on English and Japanese benchmarks.
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
With the strong representational power of large language models (LLMs), generative error correction (GER) for automatic speech recognition (ASR) aims to provide semantic and phonetic refinements to address ASR errors. This work explores how LLM-based GER can enhance and expand the capabilities of Japanese language processing, presenting the first GER benchmark for Japanese ASR with 0.9-2.6k text utterances. We also introduce a new multi-pass augmented generative error correction (MPA GER) by integrating multiple system hypotheses on the input side with corrections from multiple LLMs on the output side and then merging them. To the best of our knowledge, this is the first investigation of the use of LLMs for Japanese GER, which involves second-pass language modeling on the output transcriptions generated by the ASR system (e.g., N-best hypotheses). Our experiments demonstrated performance improvement in the proposed methods of ASR quality and generalization both in SPREDS-U1-ja and CSJ data.
citation-role summary
background 1
citation-polarity summary
fields
cs.SD 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context
An LLM-based ASR error corrector trained on synthetic rare-word speech and given simplified phonetic context lowers WER/CER and raises rare-word recall on English and Japanese benchmarks.