Emotion in speech: the acoustic attributes of fear, anger, sadness, and joy.

Abstract

Decoders can detect emotion in voice with much greater accuracy than can be achieved by objective acoustic analysis. Studies that have established this advantage, however, used methods that may have favored decoders and disadvantaged acoustic analysis. In this study, we applied several methodologic modifications for the analysis of the acoustic differentiation of fear, anger, sadness, and joy. Thirty-one female subjects between the ages of 18 and 35 (encoders) were audio-recorded during an emotion-induction procedure and produced a total of 620 emotion-laden sentences. Twelve female judges (decoders), three for each of the four emotions, were assigned to rate the intensity of one emotion each. Their combined ratings were used to select 38 prototype samples per emotion. Past acoustic findings were replicated, and increased acoustic differentiation among the emotions was achieved. Multiple regression analysis suggested that some, although not all, of the acoustic variables were associated with decoders' ratings. Signal detection analysis gave some insight into this disparity. However, the analysis of the classic constellation of acoustic variables may not completely capture the acoustic features that influence decoders' ratings. Future analyses would likely benefit from the parallel assessment of respiration, phonation, and articulation.