Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION

Abstract: 

Emotion perception is subjective and vary with respect to each individual due to the natural bias of human, such as gender, culture, and age. Conventionally, emotion recognition relies on the consensus, e.g., majority of annotations (hard label) or the distribution of annotations (soft label), and do not include rater-specific model. In this paper, we propose a joint learning methodology that simultaneously considers the label uncertainty and annotator idiosyncrasy using hard and soft emotion label annotation accompanying with individual and crowd annotator modeling. Our proposed model achieves unweighted average recall (UAR) 61.48% on the benchmark emotion corpus. Further analyses reveal that emotion perception is indeed rater-dependent, using the hard label and soft emotion distribution provides complementary affect modeling information, and finally joint learning of subjective emotion perception and individual rater model provides the best discriminative power.

up
1 user has voted: Huang-Cheng Chou

Paper Details

Authors:
Huang-Cheng Chou, Chi-Chun Lee
Submitted On:
30 May 2019 - 2:17am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Huang-Cheng Chou
Paper Code:
SLP-L9.6
Document Year:
2019
Cite

Document Files

Talk Slides

(25)

Full Paper

(21)

Subscribe

[1] Huang-Cheng Chou, Chi-Chun Lee, "EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4006. Accessed: Aug. 20, 2019.
@article{4006-19,
url = {http://sigport.org/4006},
author = {Huang-Cheng Chou; Chi-Chun Lee },
publisher = {IEEE SigPort},
title = {EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION},
year = {2019} }
TY - EJOUR
T1 - EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION
AU - Huang-Cheng Chou; Chi-Chun Lee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4006
ER -
Huang-Cheng Chou, Chi-Chun Lee. (2019). EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION. IEEE SigPort. http://sigport.org/4006
Huang-Cheng Chou, Chi-Chun Lee, 2019. EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION. Available at: http://sigport.org/4006.
Huang-Cheng Chou, Chi-Chun Lee. (2019). "EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION." Web.
1. Huang-Cheng Chou, Chi-Chun Lee. EVERY RATING MATTERS: JOINT LEARNING OF SUBJECTIVE LABELS AND INDIVIDUAL ANNOTATORS FOR SPEECH EMOTION CLASSIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4006