A probabilistic fusion strategy for audiovisual emotion recognition of sparse and noisy data

Jen Chun Lin, Chung Hsien Wu, Wen Li Wei

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

Due to diverse expression styles in real-world scenarios, recognizing human emotions is difficult without collecting sufficient and various data for model training. Besides, emotion recognition of noisy data is another challenging problem to be solved. This work endeavors to propose a fusion strategy to alleviate the problems of noisy and sparse data in bimodal emotion recognition. Toward robust bimodal emotion recognition, a Semi-Coupled Hidden Markov Model (SC-HMM) based on a state-based bimodal alignment strategy is proposed to align the temporal relation of states of two component HMMs between audio and visual streams. Based on this strategy, the SC-HMM can diminish the over-fitting problem and achieve better statistical dependency between states of audio and visual HMMs in sparse data conditions and also provides the ability to better accommodate to the noisy conditions. Experiments show a promising result of the proposed approach.

原文English
主出版物標題ICOT 2013 - 1st International Conference on Orange Technologies
頁面278-281
頁數4
DOIs
出版狀態Published - 2013 七月 12
事件1st International Conference on Orange Technologies, ICOT 2013 - Tainan, Taiwan
持續時間: 2013 三月 122013 三月 16

出版系列

名字ICOT 2013 - 1st International Conference on Orange Technologies

Other

Other1st International Conference on Orange Technologies, ICOT 2013
國家/地區Taiwan
城市Tainan
期間13-03-1213-03-16

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信

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