Problem
Research questions and friction points this paper is trying to address.
nonverbal vocalizations
temporal grounding
transcript-level tags
waveform-time boundaries
NVV-Locator: From Transcript Tags to Acoustic Boundaries for Fine-Grained Nonverbal Vocalization Grounding
🤖 AI Summary
研究提出NVV-Locator，通过非自回归填槽架构预测非言语发声的时间边界和类别，解决了现有方法对这些发声时间标记不足的问题。
📝 Abstract
Human speech includes nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, which convey affective and interactional information. Existing approaches typically represent NVVs as transcript-level tags, providing limited supervision for their waveform-time boundaries. We present NVV-Locator for fine-grained NVV temporal grounding. We first unify 26 NVV categories across public resources and construct large-scale timestamp-supervised training data through dual-LLM verification, transcript-guided forced alignment, and energy-based boundary refinement. We further introduce NVV-TimeBench, an expert-refined benchmark with 667 utterances and 1,094 events. NVV-Locator uses a non-autoregressive slot-filling architecture to jointly predict lexical timestamps, NVV categories, and event boundaries. On NVV-TimeBench, it achieves 71.0% Micro F1, 70.2% Macro F1, 80.4% Macro mIoU, and 59.6 ms Macro mMAE, outperforming the evaluated large audio model counterparts. Evaluation on an external corpus further demonstrates the cross-corpus generalization of NVV-Locator.
Innovation
Methods, ideas, or system contributions that make the work stand out.
nonverbal vocalizations
timestamp-supervised training data
non-autoregressive slot-filling architecture
NVV-TimeBench
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