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BackgroundIntelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. This study addresses the clinical problem of measuring speech intelligibility in children with velopharyngeal insufficiency (VPI) in real‐world conditions.AimsThe purpose of the study was to investigate the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) compared to typical speech. The study further determined the contribution of nasalance and articulation accuracy in judgments of intelligibility.Methods & ProceduresFifteen speakers diagnosed with VPI and their typical peers provided audio recordings of 20 sentences from the Hearing in Noise Test. Speech samples were presented over quiet and noise (+5 dB signal‐to‐noise ratio) conditions to 70 naïve listeners. Intelligibility scores from naïve listeners' orthographic transcriptions were obtained as the percentage of correctly identified words.Outcomes & ResultsA repeated‐measures analysis of variance showed diagnosis of VPI (F(1, 28) = 13.44, p = 0.001, and presence of noise (F(1, 28) = 39.18, p < 0.001) significantly affected the intelligibility scores. There was no interaction between the diagnosis of VPI and noise (F(1, 28) = 0.06, p = 0.80). The multivariate regression analysis indicated that nasalance and articulation accuracy explain a significant amount of variance in the intelligibility scores of VPI speakers in quiet (F(2, 12) = 7.11, p < 0.05, R2  = 0.55, R2Adjusted  = 0.47) and noise (F(2, 12) = 6.32, p < 0.05, R2  = 0.51, R2Adjusted  = 0.43), but the significance mainly came from the effect of percentage of consonants correct (β = 0.97, t(12) = 2.90, p = 0.01). Percentage of consonants correct significantly increased the speech intelligibility in either with or without noise conditions.Conclusions & ImplicationsThe current work suggests that background noise will significantly affect reductions in intelligibility in both groups; the effect is more prominent in VPI speech. It was also further noted that articulation accuracy significantly affected intelligibility in quiet and noise rather than nasalance scores.WHAT THIS PAPER ADDSWhat is already known on the subject Intelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. Accordingly, it is essential to determine the degree to which speech assessments in the clinic can predict communication difficulties in the presence of background noise in real life. Background noise can adversely cause speech intelligibility degradation in individuals with speech disorders.What this study adds The study examined the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) secondary to cleft palate compared to typical speech. The study results suggested that the presence of background noise will significantly affect reductions in intelligibility in both groups; however, the effect is more prominent in VPI speech.What are the clinical implications of this work? We found out that the intelligibility of VPI speech is lower in the presence of background noise, and therefore, assessments of speech intelligibility in clinical settings should take this into account. To ensure effective communication in noisy environments, recommended strategies include selecting quiet locations, eliminating potential distractions and supplementing communication with nonverbal cues. It is important to recognize that the effectiveness of these strategies may vary depending on the individual and the specific communication context.

For general parametric statistical models with some variables being contaminated with the Laplace measurement error, we propose an extrapolation algorithm to estimate the unknown parameters. By applying the conditional expectation directly to the target function, either the function to be optimized or an estimation equation, the proposed algorithm successfully bypasses the simulation step in the classical simulation extrapolation procedure and thus provides a viable alternative to the classical simulation extrapolation algorithm. Large sample properties of the resulting estimator are discussed. Finite sample performance of the proposed extrapolation estimation procedure is demonstrated by examples and numerical simulation studies.