TY - UNPB
T1 - Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning
AU - Kim, Jaebok
AU - Englebienne, Gwenn
AU - Truong, Khiet P.
AU - Evers, Vanessa
N1 - Published in the proceedings of INTERSPEECH, Stockholm, September, 2017
PY - 2017/8/13
Y1 - 2017/8/13
N2 - One of the challenges in Speech Emotion Recognition (SER) "in the wild" is the large mismatch between training and test data (e.g. speakers and tasks). In order to improve the generalisation capabilities of the emotion models, we propose to use Multi-Task Learning (MTL) and use gender and naturalness as auxiliary tasks in deep neural networks. This method was evaluated in within-corpus and various cross-corpus classification experiments that simulate conditions "in the wild". In comparison to Single-Task Learning (STL) based state of the art methods, we found that our MTL method proposed improved performance significantly. Particularly, models using both gender and naturalness achieved more gains than those using either gender or naturalness separately. This benefit was also found in the high-level representations of the feature space, obtained from our method proposed, where discriminative emotional clusters could be observed.
AB - One of the challenges in Speech Emotion Recognition (SER) "in the wild" is the large mismatch between training and test data (e.g. speakers and tasks). In order to improve the generalisation capabilities of the emotion models, we propose to use Multi-Task Learning (MTL) and use gender and naturalness as auxiliary tasks in deep neural networks. This method was evaluated in within-corpus and various cross-corpus classification experiments that simulate conditions "in the wild". In comparison to Single-Task Learning (STL) based state of the art methods, we found that our MTL method proposed improved performance significantly. Particularly, models using both gender and naturalness achieved more gains than those using either gender or naturalness separately. This benefit was also found in the high-level representations of the feature space, obtained from our method proposed, where discriminative emotional clusters could be observed.
KW - cs.CL
M3 - Preprint
BT - Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning
ER -