Comparing the acoustic expression of emotion in the speaking and the singing voice

Abstract

We examine the similarities and differences in the expression of emotion in the singing and the speaking voice. Three internationally renowned opera singers produced "vocalises" (using a schwa vowel) and short nonsense phrases in different interpretations for 10 emotions. Acoustic analyses of emotional expression in the singing samples show significant differences between the emotions. In addition to the obvious effects of loudness and tempo, spectral balance and perturbation make significant contributions (high effect sizes) to this differentiation. A comparison of the emotion-specific patterns produced by the singers in this study with published data for professional actors portraying different emotions in speech generally show a very high degree of similarity. However, singers tend to rely more than actors on the use of voice perturbation, specifically vibrato, in particular in the case of high arousal emotions. It is suggested that this may be due to by the restrictions and constraints imposed by the musical structure.

Emotion in the singing voice—a deeperlook at acoustic features in the light ofautomatic classification

“…There are a few existing studies that deal with enthusiasm in karaoke singing [15,16], which is close to the emotional dimension of arousal, or target vocal tutoring systems [17]. Previous findings in [18] suggest that the expression of emotions in speaking and singing voice are related. Further, [12] concludes that similar methods and acoustic features can be used to automatically classify emotions in speech, polyphonic music, as well as emotions perceived by listeners in or associated by them with other, general sounds.…”

“…This suggests that the methods for speech emotion recognition can be transferred to singing emotion recognition. Therefore, this paper investigates the performance of state-of-theart speech emotion recognition methods on a data set of singing voice recordings and compares this to the performance of a newly designed acoustic feature set, which is based on findings in [18].…”

“…A subset of the database of singing emotions was first introduced in [18]. Here, we use an extended set, where recordings from five additional professional opera singers have been added (eight in total) following the same protocol.…”

“…We propose a feature set based on previous, careful analysis of acoustic parameters with respect to emotional expression in the singing and speaking voice as was presented in [18]. The parameters contained in the set (referred to as EmoFt henceforth) are listed in Table 2.…”

“…This approach is adopted in the proposed feature set. Further, long-term average spectrum (LTAS)-based features used by [18] are added. Thereby, the aggregation is performed by computing the LTAS over a segment-as the arithmetic mean of the magnitude spectra of all frames (20 ms, see below) within the segment-and then computing a single vector of spectral properties from this LTAS (see Table 2 for details).…”