TY - GEN
T1 - A novel corpus developed to evaluate the impact of hospital noise on speech intelligibility
AU - Perry, Sydney
AU - Bent, Tessa
AU - Baese-Berk, Melissa
AU - Ryherd, Erica
N1 - Funding Information:
We gratefully acknowledge our funding from Indiana University including an Undergraduate Research Advanced Summer Research Scholarship, a Hutton Honors College Research Grant, and a Department of Speech, Language and Hearing Sciences Undergraduate Research Grant.
Publisher Copyright:
© INTER-NOISE 2021 .All right reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.3397/IN-2021-2064
DO - 10.3397/IN-2021-2064
M3 - Conference contribution
AN - SCOPUS:85117369915
T3 - Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering
BT - Proceedings of INTER-NOISE 2021 - 2021 International Congress and Exposition of Noise Control Engineering
A2 - Dare, Tyler
A2 - Bolton, Stuart
A2 - Davies, Patricia
A2 - Xue, Yutong
A2 - Ebbitt, Gordon
PB - The Institute of Noise Control Engineering of the USA, Inc.
T2 - 50th International Congress and Exposition of Noise Control Engineering, INTER-NOISE 2021
Y2 - 1 August 2021 through 5 August 2021
ER -