An end-to-end deep learning framework with speech emotion recognition of atypical individuals
Publication
, Conference
Tang, D; Zeng, J; Li, M
Published in: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
January 1, 2018
The goal of the ongoing ComParE 2018 Atypical Affect sub-challenge is to recognize the emotional states of atypical individuals. In this work, we present three modeling methods under the end-to-end learning framework, namely CNN combined with extended features, CNN+RNN and ResNet, respectively. Furthermore, we investigate multiple data augmentation, balancing and sampling methods to further enhance the system performance. The experimental results show that data balancing and augmentation increase the unweighted accuracy (UAR) by 10% absolutely. After score level fusion, our proposed system achieves 48.8% UAR on the develop dataset.
Duke Scholars
Published In
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
DOI
EISSN
1990-9772
ISSN
2308-457X
Publication Date
January 1, 2018
Volume
2018-September
Start / End Page
162 / 166
Citation
APA
Chicago
ICMJE
MLA
NLM
Tang, D., Zeng, J., & Li, M. (2018). An end-to-end deep learning framework with speech emotion recognition of atypical individuals. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (Vol. 2018-September, pp. 162–166). https://doi.org/10.21437/Interspeech.2018-2581
Tang, D., J. Zeng, and M. Li. “An end-to-end deep learning framework with speech emotion recognition of atypical individuals.” In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2018-September:162–66, 2018. https://doi.org/10.21437/Interspeech.2018-2581.
Tang D, Zeng J, Li M. An end-to-end deep learning framework with speech emotion recognition of atypical individuals. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 2018. p. 162–6.
Tang, D., et al. “An end-to-end deep learning framework with speech emotion recognition of atypical individuals.” Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 2018-September, 2018, pp. 162–66. Scopus, doi:10.21437/Interspeech.2018-2581.
Tang D, Zeng J, Li M. An end-to-end deep learning framework with speech emotion recognition of atypical individuals. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 2018. p. 162–166.
Published In
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
DOI
EISSN
1990-9772
ISSN
2308-457X
Publication Date
January 1, 2018
Volume
2018-September
Start / End Page
162 / 166