wav2VOT: Automatic estimation of voice onset time, closure duration, and burst realisation with wav2vec2
For phonetic researchers, wav2VOT reduces manual annotation effort by leveraging large speech models for accurate phonetic annotation.
wav2VOT uses wav2vec2 to automatically estimate voice onset time, closure duration, and burst realisation, achieving high accuracy with fine-tuning and performing comparably to current approaches on unseen datasets.
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 predictions demonstrate high fidelity across stop voicing and place of articulation. These results demonstrate that large speech models are capable of producing accurate annotations, and further motivate exploration of large speech models as tools in phonetic research pipelines.