TY - GEN
T1 - Using speech data to recognize emotion in human gait
AU - Lim, Angelica
AU - Okuno, Hiroshi G.
PY - 2012
Y1 - 2012
N2 - Robots that can recognize emotions can improve humans' mental health by providing empathy and social communication. Emotion recognition by robots is challenging because unlike in human-computer environments, facial information is not always available. Instead, our method proposes using speech and gait analysis to recognize human emotion. Previous research suggests that the dynamics of emotional human speech also underlie emotional gait (walking). We investigate the possibility of combining these two modalities via perceptually common parameters: Speed, Intensity, irRegularity, and Extent (SIRE). We map low-level features to this 4D cross-modal emotion space and train a Gaussian Mixture Model using independent samples from both voice and gait. Our results show that a single, modality-mixed trained model can perform emotion recognition for both modalities. Most interestingly, recognition of emotion in gait using a model trained uniquely on speech data gives comparable results to a model trained on gait data alone, providing evidence for a common underlying model for emotion across modalities.
AB - Robots that can recognize emotions can improve humans' mental health by providing empathy and social communication. Emotion recognition by robots is challenging because unlike in human-computer environments, facial information is not always available. Instead, our method proposes using speech and gait analysis to recognize human emotion. Previous research suggests that the dynamics of emotional human speech also underlie emotional gait (walking). We investigate the possibility of combining these two modalities via perceptually common parameters: Speed, Intensity, irRegularity, and Extent (SIRE). We map low-level features to this 4D cross-modal emotion space and train a Gaussian Mixture Model using independent samples from both voice and gait. Our results show that a single, modality-mixed trained model can perform emotion recognition for both modalities. Most interestingly, recognition of emotion in gait using a model trained uniquely on speech data gives comparable results to a model trained on gait data alone, providing evidence for a common underlying model for emotion across modalities.
KW - affect recognition
KW - emotional gait
KW - emotional voice
KW - robot emotions
UR - http://www.scopus.com/inward/record.url?scp=84867628984&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867628984&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34014-7_5
DO - 10.1007/978-3-642-34014-7_5
M3 - Conference contribution
AN - SCOPUS:84867628984
SN - 9783642340130
VL - 7559 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 52
EP - 64
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 3rd International Workshop on Human Behavior Understanding, HBU 2012
Y2 - 7 October 2012 through 7 October 2012
ER -
