This study examines stylometric consistency patterns in professional human translations of spoken Chinese to written English, using the CoVoST corpus. Moving beyond traditional translation quality assessment, the investigation studies how human translators navigate the speech-to-text modality shift and whether this process produces domain-invariant stylistic regularities. Through a framework combining stylometric analysis, machine learning, and digital humanities critique, I identify a “consistency bias” — manifested as standardized lexical diversity, flattened syntactic structures, and repetitive discourse patterns — that reveals how professional constraints and cognitive processing shape translation output. My analysis of 16,899 human-translated English sentences, with detailed statistical comparison of 300 sentences against contemporary spoken English baselines, demonstrates that speech-to-text translation exhibits significantly reduced lexical diversity (Cohen's d=0.34, p<0.001), shallower syntactic structures (d=0.33, p<0.001), and narrower modal verb usage (d=0.43, p<0.001) compared to original spoken English. These findings illuminate the cognitive and professional constraints shaping human translation practice; future research might consider how such patterns subsequently inform machine translation systems trained on human-translated corpora.
