Computer-assisted syllable analysis of continuous speech as a measure of child speech disorder

In this study, a computer-driven, phoneme-agnostic method was explored for assessing speech disorders (SDs) in children, bypassing traditional labor-intensive phonetic transcription. Using the SpeechMark® automatic syllabic cluster (SC) analysis, which detects sequences of acoustic features that characterize well-formed syllables, 1952 American English utterances of 60 preschoolers were analyzed [16 with speech disorder present (SD-P) and 44 with speech disorder not present (SD-NP)] from two dialectal areas. A four-factor regression analysis evaluated the robustness of seven automated measures produced by SpeechMark® and their interactions. SCs significantly predicted SD status (p < 0.001). A secondary analysis using a generalized linear model with a negative binomial distribution evaluated the number of SCs produced by the groups. Results highlighted that children with SD-P produced fewer well-formed clusters [incidence rate ratio (IRR) = 0.8116, p ≤ 0.0137]. The interaction between speech group and age indicated that the effect of age on syllable count was more pronounced in children with SD-P (IRR = 1.0451, p = 0.0251), suggesting that even small changes in age can have a significant effect on SCs. In conclusion, speech status significantly influences the degree to which preschool children produce acoustically well-formed SCs, suggesting the potential for SCs to be speech biomarkers for SD in preschoolers.