Relative Time Intervals Representation for Word-level Timestamping with Masked Training
Although Speech Large Language Models (SpeechLLMs) excel at speech understanding and generation, their capacity for fine-grained, temporally aligned outputs remains underexplored. Our work addresses this gap by enabling SpeechLLMs to jointly model speech content and temporal structure, effectively transforming them from ``content understanding machines" into ``temporal-aware content understanding machines". Specifically, we replace traditional absolute timestamps with relative timestamps, achieving a more compact vocabulary and stronger generalization capabilities. To efficiently infuse timestamp prediction ability into pre-trained large language models, we introduce a hybrid fine-tuning ...
| Authors | Quanwei Tang, Zhiyu Tang, Xu Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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