Papers by Gakuto Kurata
Robust ASR Error Correction with Conservative Data Filtering (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Error correction (EC) based on large language models is an emerging technology to enhance the performance of automatic speech recognition systems. |
| Approach: | They propose to pair large set of ASR hypotheses with gold references to improve linguistic acceptability over sources and be inferable from available context. |
| Outcome: | The proposed approach significantly reduces overcorrection and improves quality in out-of-domain (OOD) settings. |
Speech-enriched Memory for Inference-time Adaptation of ASR Models to Word Dictionaries (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing contextual biasing techniques require additional parameterization . state-of-the-art ASR systems often fail to recognize named entities or critical rare words . |
| Approach: | They propose an algorithm that uses nearest-neighbor matching to predict ASR models . a list of rare entities is indexed in a memory and then stored the best possible match . |
| Outcome: | The proposed algorithm improves the prediction of state-of-the-art ASR models on rare words . it prevents spurious matches by restricting to word-level matches . |