Dissertation > Excellent graduate degree dissertation topics show

Research on Multi-modal Fusion Emotion Recognition

Author: CaoTianZuo
Tutor: WangJianRong
School: Tianjin University
Course: Computer Science and Technology
Keywords: Emotion recognition Multi-modal Fusion HMM ANN
CLC: TN912.34
Type: Master's thesis
Year: 2012
Downloads: 6
Quote: 0
Read: Download Dissertation

Abstract


Emotion plays an important role in human communications. Changes of affectivestates have an impact on people’s perception and decision. Emotion recognition is asignificant research field of pattern recognition. It introduces emotion intohuman-computer interaction. People express their emotions through facial expressions,speech, postures, physiological signals, characters, and so on. As a result, emotionrecognition is inherently a matter of multi-modal fusion.In this paper, we construct a framework of multi-modal fusion emotionrecognition. Facial expression features and speech features are respectively extractedfrom image sequences and speech signals. An emotion classifier is designed to fusefacial expression and speech modalities based on Hidden Markov Models andMulti-layer Perceptron. In order to locate and track facial feature points, we constructan Active Appearance Model for facial images with all kinds of expressions. FacialAnimation Parameters are calculated from motions of facial feature points asexpression features. We extract short-term mean energy, fundamental frequency andformant frequencies from each speech frame as speech features. We use expressionfeatures and speech features to train Hidden Markov Models based on ViterbiAlgorithm. Multi-layer Perceptron which fuses expression and speech modalities istrained based Back-propagation Algorithm. Experiments indicate that multi-modalfusion emotion recognition algorithm which is presented in this paper has relativelyhigh recognition accuracies.The proposed algorithm makes use of affective information from video and audio.The approach has better performance and robustness than methods using only videoor audio separately.

Related Dissertations

  1. Multiple ANN/HMM Hybrid Used in Speech Recognition,TN912.34
  2. Speech Emotion Recognition Based on Multifractal,TN912.34
  3. Research on Feature Selection and Construction in Emotion Speech Recognition,TP18
  4. Measurement and Reconstruction of Ion Channel Current for Cell Membrane in Rat’s Cerebral Cortex,Q42
  5. A Research on Speech Recognition Technology,TN912.34
  6. Research of Handwriting Input Method Based on Computer Vision,TP391.14
  7. Fatigue Reliability Sensitivity Analysis and Reliability Robust Design of High Speed Train Wheel,U270.33
  8. The Research for Fractal Feature Extraction and Face Recognition Algorithm,TP391.41
  9. Reliability-based Sensitivity Research on Multi-span Rotor System,TH113
  10. Voice recognition in the access control application,TN912.34
  11. Rule-based Web text information extraction technology research,TP391.1
  12. Research on Emotion Recognition of Body Motions Based on the Shape of Gaussian,TP391.41
  13. HMM-based Connection between the Social Network Analysis,F49
  14. The Research on Fault Diagnosis Method Based on Continuous Hidden Markov Model with Feature Weighted,TH165.3
  15. Research on Video Forgery Passive Detection Based on Spatial-Temporal Feature,TP391.41
  16. EEG -based emotion recognition,TP391.4
  17. Complex environment speech enhancement method,TN912.35
  18. An Hmm-based Speech Synthesis System Applied to Tianjin Dialect,TN912.33
  19. Research and Development on Mobile Speech Emotion Recognition System,TN912.34
  20. A Survey of Hidden Markov Model Based Clustering Algorithm on Time Series,TP311.13
  21. Source Apportionment, Concentration Prediction, and Meteorological Effect of Particulate Matter (PM10) in Urban Air,X513

CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing > Speech Recognition and equipment
© 2012 www.DissertationTopic.Net  Mobile