A novel corpus developed to evaluate the impact of hospital noise on speech intelligibility

Hospital noise often exceeds recommended sound levels set by health organizations leading to reductions in speech intelligibility and potential communication breakdowns between doctors and patients. However, quantifying the impact of hospital noise on intelligibility has been limited by stimuli employed in prior studies, which did not include medically related terminology. To address this gap, a corpus of medically related sentences was developed. Word frequency, word familiarity, and sentence predictability, factors known to impact intelligibility of speech, were quantified. Eight hundred words were selected from the Merriam-Webster Medical Dictionary. Word frequency was taken from SUBLEX-US, a 51-million-word corpus of American subtitles (Brysbaert & New, 2009). Word familiarity was rated by 41 monolingual listeners. The words were then used to construct 200 sentences. To determine sentence predictability, the sentences were presented to 48 participants with one word missing; their task was to fill in the missing word. Three 40 item sentence sets with different familiarity/frequency types (low/low, high/low, high/high) were selected, all with low predictability levels. These sentences and 40 standard speech perception sentences were recorded by two male and two female talkers. This corpus can be used to assess how hospital noise impacts intelligibility across listener populations.