Acoustic models of emotions may benefit from considering the underlying voice production mechanism. This study sought to describe emotional expressions according to physiological variations measured from the inverse-filtered glottal waveform in addition to standard parameter extraction. An acoustic analysis was performed on a subset of the /a/ vowels within the GEMEP database (10 speakers, 5 emotions). of the 12 acoustic features computed, repeated measures ANOVA showed significant main effects for 11 parameters. Subsequent principal components analysis revealed the three components that explain acoustic variations due to emotion, including “tension” (CQ, H1-H2, MFDR, LTAS) “perturbation” (jitter, shimmer, HNR), and “voicing” (fundamental frequency).