Emotions have strong effects on the voice production mechanisms and consequently on voice characteristics. The magnitude of these effects, measured using voice source parameters, and the interdependencies among parameters have not been examined. To better understand these relationships, voice characteristics were analyzed in 10 actors' productions of a sustained/a/vowel in five emotions. Twelve acoustic parameters were studied and grouped according to their physiological backgrounds, three related to subglottal pressure, five related to the transglottal airflow waveform derived from inverse filtering the audio signal, and four related to vocal fold vibration. Each emotion appeared to possess a specific combination of acoustic parameters reflecting a specific mixture of physiologic voice control parameters. Features related to subglottal pressure showed strong within-group and between-group correlations, demonstrating the importance of accounting for vocal loudness in voice analyses. Multiple discriminant analysis revealed that a parameter selection that was based, in a principled fashion, on production processes could yield rather satisfactory discrimination outcomes (87.1 percent based on 12 parameters and 78 percent based on three parameters). The results of this study suggest that systems to automatically detect emotions use a hypothesis-driven approach to selecting parameters that directly reflect the physiological parameters underlying voice and speech production.

Research on the vocal expression of emotion has long since used a "fishing expedition" approach to find acoustic markers for emotion categories and dimensions. Although partially successful, the underlying mechanisms have not yet been elucidated. To illustrate that this research can profit from considering the underlying voice production mechanism, we specifically analyzed short affect bursts (sustained/a/vowels produced by 10 professional actors for five emotions) according to physiological variations in phonation (using acoustic parameters derived from the acoustic signal and the inverse filter estimated voice source waveform). Results show significant emotion main effects for 11 of 12 parameters. Subsequent principal components analysis revealed three components that explain acoustic variations due to emotion, including "tension," "perturbation," and "voicing frequency." These results suggest that future work may benefit from theory-guided development of parameters to assess differences in physiological voice production mechanisms in the vocal expression of different emotions. (C) 2011 Elsevier B.V. All rights reserved.

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).