wav2VOT: Automatic estimation of voice onset time, closure duration, and burst realisation with wav2vec2
While automatic tools for speech annotation are now commonplace within phonetic research pipelines, many tasks require substantial manual correction or training sets to perform accurately. Simultaneously, large speech models such as wav2vec2 have been shown to perform well at speech classification tasks, raising the question of how these models may be applied to phonetic annotation tasks. We introduce wav2VOT: a tool for the automatic estimation of voice onset time, closure duration, and burst realisation using wav2vec2. We demonstrate that wav2VOT performs comparably with current approaches on unseen datasets, and can estimate with high accuracy with fine-tuning. Analysis of wav2VOT pred...
| Authors | James Tanner, Morgan Sonderegger, Jane Stuart-Smith et al. |
|---|---|
| Primary category | cs.SD |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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