Abstract
| Language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer/Verlag |
| Pages | 672-686 |
| Number of pages | 15 |
| DOIs | |
| State | Published - 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10271 |
Fingerprint
Keywords
- Emotion recognition
- Experiments
- Interpersonal stance
- Voice
Cite this
}
Human vs. Computer performance in voice-based recognition of interpersonal stance. / Formolo, Daniel; Bosse, Tibor.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer/Verlag, 2017. p. 672-686 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10271).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Human vs. Computer performance in voice-based recognition of interpersonal stance
AU - Formolo,Daniel
AU - Bosse,Tibor
PY - 2017
Y1 - 2017
N2 - © 2017, Springer International Publishing AG. This paper presents an algorithm to automatically detect interpersonal stance in vocal signals. The focus is on two stances (referred to as ‘Dominant’ and ‘Empathic’) that play a crucial role in aggression de-escalation. To develop the algorithm, first a database was created with more than 1000 samples from 8 speakers from different countries. In addition to creating the algorithm, a detailed analysis of the samples was performed, in an attempt to relate interpersonal stance to emotional state. Finally, by means of an experiment via Mechanical Turk, the performance of the algorithm was compared with the performance of human beings. The resulting algorithm provides a useful basis to develop computer-based support for interpersonal skills training.
AB - © 2017, Springer International Publishing AG. This paper presents an algorithm to automatically detect interpersonal stance in vocal signals. The focus is on two stances (referred to as ‘Dominant’ and ‘Empathic’) that play a crucial role in aggression de-escalation. To develop the algorithm, first a database was created with more than 1000 samples from 8 speakers from different countries. In addition to creating the algorithm, a detailed analysis of the samples was performed, in an attempt to relate interpersonal stance to emotional state. Finally, by means of an experiment via Mechanical Turk, the performance of the algorithm was compared with the performance of human beings. The resulting algorithm provides a useful basis to develop computer-based support for interpersonal skills training.
KW - Emotion recognition
KW - Experiments
KW - Interpersonal stance
KW - Voice
U2 - 10.1007/978-3-319-58071-5_51
DO - 10.1007/978-3-319-58071-5_51
M3 - Chapter
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 672
EP - 686
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer/Verlag
ER -