Speech connectedness and linguistic complexity as predictors of Chinese—English bilinguals' interpreting performance

Interpreting provides a unique window into bilingual processing, as it requires the rapid transfer of meaning across languages under high temporal and cognitive constraints. This study investigates speech connectedness and linguistic complexity as predictors of interpreting performance in Chinese–English non-interpreter bilinguals. Consecutive interpreting data from 917 participants in the Parallel Corpus of Chinese EFL Learners (PACCEL) were analyzed using two computational tools: SpeechGraphs, to measure speech connectedness, and Coh-Metrix, to assess linguistic complexity. Results showed that higher interpreting scores were associated with greater long-range connectedness and fewer short-range recurrences, suggesting that more fluent and coherent speech reflects more efficient cognitive processing. Coh-Metrix indices further revealed that better-performing participants produced texts containing less frequent words, higher lexical specificity, and were more complex. Together, these findings indicate that measures of speech connectedness and linguistic complexity might serve as reliable predictors of interpreting competence in Chinese–English non-interpreter bilinguals. By integrating large-scale learner data with computational analyses, the study highlights how linguistic output can index underlying cognitive–linguistic mechanisms in bilingual interpreting.