Investigating Perceptual Biases, Data Reliability, and Data Discovery in a Methodology for Collecting Speech Errors From Audio Recordings

This work describes a methodology of collecting speech errors from audio recordings and
investigates how some of its assumptions affect data quality and composition. Speech errors of all
types (sound, lexical, syntactic, etc.) were collected by eight data collectors from audio recordings
of unscripted English speech. Analysis of these errors showed that: (i) different listeners find
different errors in the same audio recordings, but (ii) the frequencies of error patterns are similar
across listeners; (iii) errors collected “online” using on the spot observational techniques are more
likely to be affected by perceptual biases than “offline” errors collected from audio recordings;
and (iv) datasets built from audio recordings can be explored and extended in a number of ways
that traditional corpus studies cannot be.