Transforming assessment of speech in children with cleft palate via online crowdsourcing.

OBJECTIVE

Speech intelligibility is fundamental to social interactions and a critical surgical outcome in patients with cleft palate. Online crowdsourcing is a burgeoning technology, with potential to mitigate the burden of limited accessibility to speech-language-pathologists (SLPs). This pilot study investigates the concordance of online crowdsourced evaluations of hypernasality with SLP ratings of children with cleft palate.

METHODS

Six audio-phrases each from children with cleft palate were assessed by online crowdsourcing using Amazon Mechanical Turk (MTurk), and compared to SLP's gold-standard hypernasality score on the Pittsburgh Weighted Speech Score (PWSS). Phrases were presented to MTurk crowdsourced lay-raters to assess hypernasality on a Likert scale analogous to the PWSS. The survey included clickable reference audio samples for different levels of hypernasality.

RESULTS

1,088 unique online crowdsourced speech ratings were collected on 16 sentences of 3 children with cleft palate aged 4-8 years, with audio averaging 6.5 years follow-up after cleft palate surgery. Patient 1 crowd-mean was 2.62 (SLP rated 2-3); Patient 2 crowd-mean 2.66 (SLP rated 3); and Patient 3 crowd-mean 1.76 (SLP rated 2). Rounded for consistency with PWSS scale, all patients matched SLP ratings. Different sentences had different accuracies compared to the SLP gold standard scores.

CONCLUSION

Online crowdsourced ratings of hypernasal speech in children with cleft palate were concordant with SLP ratings, predicting SLP scores in all 3 patients. This novel technology has potential for translation in clinical speech assessments, and may serve as a valuable screening tool for non-experts to identify children requiring further assessment and intervention by a qualified speech language pathology expert.