This study investigates listeners’ ability to detect emotion from a diverse set of speech samples, including both spontaneous conversations and actor-posed speech. It explores the contributions of lexical content and acoustic properties when native listeners rate seven pairs of affective attributes. Two experimental conditions were employed: a text condition, where participants evaluated emotional attributes from written transcripts without vocal information, and a voice condition, where participants listened to audio recordings to assess emotions. Results showed that the importance of lexical and vocal cues varies across 14 affective states for posed and spontaneous speech. Vocal cues enhanced the expression of sadness and anger in posed speech, while they had less impact on conveying happiness. Notably, vocal cues tended to mitigate negative emotions conveyed by the lexical content in spontaneous speech. Further analysis on correlations between emotion ratings in text and voice conditions indicated that lexical meanings suggesting anger or hostility could be interpreted as positive affective states like intimacy or confidence. Linear regression analyses indicated that emotional ratings by native listeners could be predicted up to 59% by lexical content and up to 26% by vocal cues. Listeners relied more on vocal cues to perceive emotional tone when the lexical content was ambiguous in terms of feeling and attitude. Finally, the analysis identified statistically significant basic acoustical parameters and other non/para-linguistic information, after controlling for the effect of lexical content.
