Papers by Ji-Hwan Kim
SEAM: Bridging the Temporal-Semantic Granularity Gap for LLM-based Speech Recognition (2026.findings-eacl)
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| Challenge: | Existing duration-based methods generate embeddings at fixed rates, creating distributional mismatch with LLM pre-training. |
| Approach: | They propose an encoder-decoder architecture that generates embeddings at variable rates through cross-attention between speech features and text embeddables. |
| Outcome: | The proposed architecture achieves competitive performance on LibriSpeech (2.6%/5.2% WER) and 4.7% WER on TED-LIUM-v2 with a multi-stage training strategy and First Token Guidance. |