English vowel identification in noise shows reduced native-language influence: a behavioral study using machine-learning analysis of Chinese children

Abstract

Non-native listeners experience greater difficulty perceiving second-language (L2) speech in noise, yet whether first-language (L1) phonological skills continue to support L2 perception under adverse acoustic conditions remains unclear. We examined English back-vowel identification in 52 Chinese fifth- ( n = 25) and sixth-grade ( n = 27) children under quiet, 5 dB SNR, and −5 dB SNR conditions. Chinese vowel identification in quiet was used to index L1 ability, and linear regression and random forest models were used to characterize associations among acoustic background, grade, and L1 perception. Sixth graders outperformed fifth graders across conditions, but the grade advantage decreased as noise increased, with accuracy differences of 13.30%, 9.80%, and 3.20%, respectively. Chinese vowel identification significantly predicted English vowel identification in quiet but showed no significant predictive association under either noise condition. Random forest analyses complemented the specified linear model by revealing nonlinear predictive patterns, including a grade-by-noise interaction not detected by the linear model in this sample. These exploratory findings should be interpreted cautiously given the modest sample size ( N = 52) and strict participant-level validation. The findings suggest that the association between quiet-measured L1 vowel identification and L2 vowel identification is context-dependent and attenuates under noisy listening conditions. They also indicate that grade-related advantages diminish as acoustic difficulty increases, while machine-learning analyses may provide complementary information about predictive patterns without establishing underlying cognitive mechanisms.