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Recognizing emotions in the singing voice
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.
2017 (English)In: Psychomusicology, ISSN 0275-3987, E-ISSN 2162-1535, Vol. 27, no 4, p. 244-255Article in journal (Refereed) Published
Abstract [en]

Although the human ability to recognize emotions in vocal speech utterances with reasonable accuracy has been well documented in numerous studies, little research has been reported on emotion recognition from emotional expression in the singing voice. This paper is the first to examine this issue by asking internationally known professional opera singers to portray 9 major emotions by singing sequences of nonsense syllables on the standard musical scale. We then asked more than 500 hundred listener/judges from different cultures with a wide range of musical preferences and degree of musical knowledge to recognize the intended emotions from the voice recordings. The data show that listeners are indeed able to recognize emotions expressed in singing with better-than-chance accuracy. In addition, we find some evidence that there seem to be only minor effects of culture or language on the ability to recognize the emotional interpretations. Some emotions are more easily recognized than others are. Overall, recognition ability from the singing voice compares well to accuracy rates in studies using speaking. Judges clearly use the differential acoustic patterns of sound generated by the singers in their performance to infer the emotion expressed, as demonstrated by comparing the recognition rates for different emotions to results of statistical classification based on acoustic parameters. We also attempt to explore the nature of the inference process by examining, using path models, the major acoustic variables involved and the inference from subjectively perceived configurations of voice quality.

Place, publisher, year, edition, pages
2017. Vol. 27, no 4, p. 244-255
National Category
Musicology Psychology Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259542DOI: 10.1037/pmu0000193OAI: oai:DiVA.org:kth-259542DiVA, id: diva2:1351993
Note

QC 20191009

Available from: 2019-09-17 Created: 2019-09-17 Last updated: 2019-10-09Bibliographically approved

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Sundberg, Johan

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