Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means
of transcription tasks (‘type out what you hear’). The intelligibility of a given stimulus is then often
expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring
the participants’ raw responses for words correctly identified from the target sentence is a time-
consuming task, and hence resource-intensive. Moreover, there is no consensus among speech
scientists about what specific protocol to use for the human scoring, limiting the reliability of
human scores. The present paper evaluates various forms of fuzzy string matching between
participants’ responses and target sentences, as automated metrics of listener transcript accuracy.
We demonstrate that one particular metric, the Token Sort Ratio, is a consistent, highly efficient,
and accurate metric for automated assessment of listener transcripts, as evidenced by high
correlations with human-generated scores (best correlation: r = 0.940) and a strong relationship to
acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for
assessment of listener transcript accuracy in large-scale speech intelligibility studies. See
https://tokensortratio.netlify.app for an online implementation.