会议论文

Huang, D. -Y.

英文

18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION

2308-457X

2017

162-165

18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017)

Interspeech

AUG 20-24, 2017

Stockholm, SWEDEN

[Huang, D. -Y.;Xu, Mingyu;Ming, Huaiping;Dong, Minghui] Inst Infocomm Res, Human Language Technol Dept, Singapore, Singapore.^[Ding, Wan;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Li, Haizhou] Natl Univ Singapore, ECE Dept, Singapore, Singapore.

Int Speech Commun Assoc, Stockholm Univ, KTH Royal Inst Technol, Karolinska Inst, Amazon Alexa, DiDi, Furhat Robot, Microsoft, EZ Alibaba Grp, CIRRUS LOGIC, CVTE, Google, Baidu, IBM Res, YAHOO Japan, Nuance, Voice Provider, ASM Solut Ltd, Mitsubishi Elect Res Lab, Yandex

C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE

ISCA-INT SPEECH COMMUNICATION ASSOC

978-1-5108-4876-4

10.21437/Interspeech.2017-1088

本校为其他机构

This paper presents a multimodal approach to predict affective dimensions, that makes full use of features from audio. video. Electrodermal Activity (EDA) and Electrocardiogram (ECG) using three regression techniques such as support vector regression (SVR). partial least squares regression (PLS), and a deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN) regression. Each of the three regression techniques performs multimodal affective dimension prediction followed by a fusion of different models on features of four modalities using a support vector regression. A supp...