Assessing automatic VOT annotation using unimpaired and impaired speech

Esteban Buz, Adam Buchwald, Tzeviya Fuchs, Joseph Keshet

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)624-634
Number of pages11
JournalInternational Journal of Speech-Language Pathology
Issue number6
StatePublished - Oct 16 2018


  • Annotation
  • speech
  • technology

ASJC Scopus subject areas

  • Research and Theory
  • Otorhinolaryngology
  • Language and Linguistics
  • LPN and LVN
  • Speech and Hearing


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