@ARTICLE{6507321,
<br>
author={H. Meng and N. Bianchi-Berthouze},
<br>  journal={IEEE Transactions on Cybernetics}, 
<br> title={Affective State Level Recognition in Naturalistic Facial and Vocal Expressions}, 
<br>  year={2014},
<br>  volume={44},
<br>  number={3},
<br>  pages={315-328},
<br>  keywords={audio signal processing;emotion recognition;face recognition;feature extraction;hidden Markov models;image classification;learning (artificial intelligence);matrix algebra;speech recognition;AVEC 2011 audio-and-video dataset;HMM;PAINFUL video dataset;affective expression features;affective state level recognition;classification stages;continuous naturalistic affective expressions;discrete emission matrices;discrete transition matrices;emotion recognition;first-order Markov models;hidden Markov model framework;machine learning;naturalistic facial expressions;naturalistic vocal expressions;path-finding problem;probability;soft decision value;Affective computing;HMM;continuous emotion recognition;dimensional model of affect;machine learning;naturalistic affective expressions},
<br>  doi={10.1109/TCYB.2013.2253768},
<br>  ISSN={2168-2267},
<br>  month={March},}<br>