AUTHOR=O'Farrell Ciara , McCabe Patricia , Purcell Alison , Heard Rob TITLE=The Adult Perceptual Limen of Syllable Segregation in Typically Developing Paediatric Speech JOURNAL=Frontiers in Communication VOLUME=7 YEAR=2022 URL=https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2022.839415 DOI=10.3389/fcomm.2022.839415 ISSN=2297-900X ABSTRACT=

Inappropriate gaps between syllables are one of the core diagnostic features of both childhood apraxia of speech and acquired apraxia of speech. However, little is known about how listeners perceive and identify inappropriate pauses between syllables (gap detection). Only one previous study has investigated the perception of inappropriate pauses between syllables in typical adult speakers and no investigations of gap detection in children's speech have been undertaken. The purpose of this research was to explore the boundaries of listener gap detection to determine at which gap length (duration) a listener can perceive that an inappropriate pause is present in child speech. Listener perception of between-syllable gaps was explored in an experimental design study using the online survey platform Qualtrics. Speech samples were collected from two typically developing children and digitally manipulated to insert gaps between syllables. Adult listeners (n = 84) were recruited and could accurately detect segregation on 80% of presentations at a duration between 100 and 125 ms and could accurately detect segregation on 90% of presentations at a duration between 125 and 150 ms. Listener musical training, gender and age were not correlated with accuracy of detection, but speech pathology training was, albeit weakly. Male speaker gender, and strong onset syllable stress were correlated with increased accuracy compared to female speaker gender and weak onset syllable stress in some gap conditions. The results contribute to our understanding of speech acceptability in CAS and other prosodic disorders and moves towards developing standardised criteria for rating syllable segregation. There may also be implications for computer and artificial intelligence understanding of child speech and automatic detection of disordered speech based on between syllable segregation.