Step-wise emotion recognition using concatenated-HMM

Authors:
Derya Ozkan;Stefan Scherer;Louis-Philippe Morency
Affiliations:
University of Southern California, Playa Vista, CA, USA;University of Southern California, Playa Vista, CA, USA;University of Southern California, Playa Vista, CA, USA
Venue:
Proceedings of the 14th ACM international conference on Multimodal interaction
Year:
2012

Citing 11
Cited 0

HMM Based On-Line Handwriting Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding, Analysis, Interpretation, and Recognition of Facial Expressions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal Human Emotion/Expression Recognition

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Speech and Language Processing (2nd Edition)

Speech and Language Processing (2nd Edition)
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio-Visual Classification and Fusion of Spontaneous Affective Data in Likelihood Space

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Affective computing: a review

ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Human expression recognition from motion using a radial basis function network architecture

IEEE Transactions on Neural Networks
Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification

Computer Speech and Language
AVEC 2012: the continuous audio/visual emotion challenge - an introduction

Proceedings of the 14th ACM international conference on Multimodal interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

Human emotion is an important part of human-human communication, since the emotional state of an individual often affects the way that he/she reacts to others. In this paper, we present a method based on concatenated Hidden Markov Model (co-HMM) to infer the dimensional and continuous emotion labels from audio-visual cues. Our method is based on the assumption that continuous emotion levels can be modeled by a set of discrete values. Based on this, we represent each emotional dimension by step-wise label classes, and learn the intrinsic and extrinsic dynamics using our co-HMM model. We evaluate our approach on the Audio-Visual Emotion Challenge (AVEC 2012) dataset. Our results show considerable improvement over the baseline regression model presented with the AVEC 2012.