@article {472,
	title = {Automatic assessment of transcript accuracy for speech intelligibility studies},
	year = {2020},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {In the field of speech perception, many studies assess the intelligibility of spoken stimuli by means of verbal repetition ({\textquoteleft}repeat back what you hear{\textquoteright}) or transcription tasks ({\textquoteleft}type out what you hear{\textquoteright}). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target stimulus. Yet scoring the participants{\textquoteright} raw transcripts for words correctly identified from the target stimulus is a time-consuming task, and hence resource-intensive. Moreover, there is no consensus on what protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of {\textquoteleft}fuzzy string matching{\textquoteright} between participants{\textquoteright} responses and target sentences as automated metrics of listener transcript accuracy. Fuzzy string matching is identified as a consistent, efficient, and accurate method for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (highest r = 0.94) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for speech scientists, allowing fast and reliable assessment of listener transcript accuracy in large-scale speech intelligibility studies.},
	author = {Hans Rutger Bosker}
}
