Assessing automatic VOT annotation using unimpaired and impaired speech

Abstract

Investigating speech processes often involves analysing data gathered by phonetically annotating speech recordings. Yet, the manual annotation of speech can often be resource intensive—requiring substantial time and labour to complete. Recent advances in automatic annotation methods offer a way to reduce these annotation costs by replacing manual annotation. For researchers and clinicians, the viability of automatic methods depends whether one can draw similar conclusions about speech processes from automatically annotated speech as one would from manually annotated speech. Here, we evaluate how well one automatic annotation tool, AutoVOT, can approximate manual annotation. We do so by comparing analyses of automatically and manually annotated speech in two studies. We find that, with some caveats, we are able to draw the same conclusions about speech processes under both annotation methods. The findings suggest that automatic methods may be a viable way to reduce phonetic annotation costs in the right circumstances. We end with some guidelines on if and how well AutoVOT may be able to replace manual annotation in other data sets.

Keywords:

• Annotation
• technology
• speech

Notes

1. Due to differences in token-based exclusions and the maximal converging random effects structure, the values reported here slightly differ from those reported in Buz (2016).