Deriving gradient measures of child speech from crowdsourced ratings.

Recent research has demonstrated that perceptual ratings aggregated across multiple non-expert listeners can reveal gradient degrees of contrast between sounds that listeners might transcribe identically. Aggregated ratings have been found to correlate strongly with acoustic gold standard measures both when individual raters use a continuous rating scale such as visual analog scaling (Munson et al., 2012) and when individual raters provide binary ratings (McAllister Byun, Halpin, & Szeredi, 2015).

Finding the experts in the crowd: Validity and reliability of crowdsourced measures of children's gradient speech contrasts.

Perceptual ratings aggregated across multiple nonexpert listeners can be used to measure covert contrast in child speech. Online crowdsourcing provides access to a large pool of raters, but for practical purposes, researchers may wish to use smaller samples. The ratings obtained from these smaller samples may not maintain the high levels of validity seen in larger samples.

Direction of attentional focus in biofeedback treatment for /r/ misarticulation.

Background: Maintaining an external direction of focus during practice is reported to facilitate acquisition of non-speech motor skills, but it is not known whether these findings also apply to treatment for speech errors. This question has particular relevance for treatment incorporating visual biofeedback, where clinician cueing can direct the learner's attention either internally (i.e.

Online crowdsourcing for efficient rating of speech: a validation study.

Unlabelled: Blinded listener ratings are essential for valid assessment of interventions for speech disorders, but collecting these ratings can be time-intensive and costly. This study evaluated the validity of speech ratings obtained through online crowdsourcing, a potentially more efficient approach. 100 words from children with /r/ misarticulation were electronically presented for binary rating by 35 phonetically trained listeners and 205 naïve listeners recruited through the Amazon Mechanical Turk (AMT) crowdsourcing platform.