On not remembering disfluencies

Disfluencies repetitions and reformulations midsentence in normal spontaneous speech are problematic for both psychological and computational models of speech understanding. Much effort is being applied to finding ways of adapting computational systems to detect and delete disfluencies. The input to such systems is usually an accurate transcription. We present results of an experiment in which human listeners are asked to give verbatim transcriptions of disfluent and fluent utterances. These suggest that listeners are seldom able to identify all the words “deleted” in disfluencies. While all types suffer, identification rates for repetitions are even worse than for other types. We attribute the results to difficulties in recall or coding for recall items which can not be identified with certainty. This inability seems to make human speech recognition more robust than current computational models.