The possibility of cultural differences in the fundamental acoustic patterns used to express emotion through the voice is an unanswered question central to the larger debate about the universality versus cultural specificity of emotion. This study used emotionally inflected standard-content speech segments expressing 11 emotions produced by 100 professional actors from 5 English-speaking cultures. Machine learning simulations were employed to classify expressions based on their acoustic features, using conditions where training and testing were conducted on stimuli coming from either the same or different cultures. A wide range of emotions were classified with above-chance accuracy in cross-cultural conditions, suggesting vocal expressions share important characteristics across cultures. However, classification showed an in-group advantage with higher accuracy in within- versus cross-cultural conditions. This finding demonstrates cultural differences in expressive vocal style, and supports the dialect theory of emotions according to which greater recognition of expressions from in-group members results from greater familiarity with culturally specific expressive styles.