wav2VOT: Automatic estimation of voice onset time, closure duration, and burst realisation with wav2vec2

2026-06-27

AI Summary

This work addresses the limitations of traditional phonetic annotation tools, which often require extensive manual correction or task-specific training data to accurately estimate key phonetic parameters such as voice onset time (VOT), closure duration, and burst realization. To overcome these challenges, the paper introduces wav2VOT, the first approach to directly leverage large-scale pretrained speech models like wav2vec 2.0 for fine-grained phonetic feature annotation. By employing segment-level regression and classification fine-tuning strategies, wav2VOT enables joint, high-precision estimation of these parameters. The method substantially reduces reliance on human intervention and labeled task-specific data, demonstrates strong generalization across unseen datasets, and maintains high predictive fidelity across varying voicing contrasts and places of articulation.

Abstract

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.

Problem

• voice onset time
• phonetic annotation
• automatic speech analysis
• closure duration
• burst realisation

Innovation

• voice onset time
• wav2vec2
• automatic phonetic annotation
• closure duration
• burst realisation