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Modeling Human Behaviors in Psychology Using Engineering Methods.
The main purpose of the work is to showcase the interdisciplinary engineering approaches in modeling and understanding human behaviors during interpersonal interactions those that could be typical, distressed, or atypical. The ability to measure human behaviors quantitatively has been a core compone...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Aalborg :
River Publishers,
2014.
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| Colección: | River Publishers series in information science and technology.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Half Title; Series Page; Title Page; Copy Right; Contents; Part I
- Modeling Human Behaviors:An Engineering Approach; Chapter 1
- Behavioral Signal Processing (BSP): Behavioral Informatics; 1.1 BSP: Introduction; 1.1.1 BSP: Technical Challenges and Complexities; 1.2 BSP: Computational Methods for Dyadic InteractionDynamics; 1.2.1 BSP: Further Complexities in Modeling Interaction Dynamics; Chapter 2
- Applications in Modeling Human Behaviors Computationally; 2.1 BSP Application Domains; 2.2 Case Study I: Emotion Recognition from Speech.
- 2.3 Case Study II: Quantifying Implicit Vocal Entrainment inHuman Interactions2.4 Case Study III: Data-driven Perceptual Experiment; Part II
- Affective Computing fromSpeech; Chapter 3
- Individual Utterance Emotion Recognition; 3.1 Introduction; 3.2 Emotion Databases and Acoustic Feature Extraction; 3.2.1 The AIBO Database; 3.2.2 The USC IEMOCAP Database; 3.2.3 Acoustic Feature Extraction; 3.2.4 Feature Selection and Normalization; 3.3 Emotion Classification Framework; 3.3.1 Building the Hierarchical Decision Tree.
- 3.3.2 Building the Hierarchical Decision Tree for the AIBO Database andthe USC IEMOCAP Database3.3.3 Classifier for Binary Classification Tasks; 3.3.3.1 Bayesian Logistic Regression; 3.4 Emotion Recognition Experiment Setup and Results; 3.4.1 The AIBO Database; 3.4.1.1 Results of Experiment I on the AIBO database; 3.4.1.2 Results of Experiment II on the AIBO database; 3.4.2 The USC IEMOCAP Database; 3.4.2.1 Experiment Result of the USC IEMOCAP Database; 3.5 Conclusions and Future Work; Chapter 4
- Dialog-based Emotion Recognition; 4.1 Introduction; 4.2 Emotion Database and Annotation.
- 4.2.1 The USC IEMOCAP Database4.2.2 Emotion Annotation; 4.3 Dynamic Bayesian Network Model; 4.4 Experimental Results and Discussion; 4.4.1 Acoustic Feature Extraction; 4.4.2 Experiment Setup; 4.4.3 Experiment Results and Discussion; 4.5 Conclusions and Future Work; Part III Quantifying Human Behavior in Psychology; Chaper 5
- Implicit Vocal Synchrony Quantification; 5.1 Introduction; 5.2 BSP Database: The Couple Therapy Corpus; 5.2.1 Pre-processing and Audio Feature Extraction; 5.2.2 Behavioral Codes of Interest; 5.3 Signal-derived Vocal Entrainment Quantification.
- 5.3.1 PCA-based Similarity Measures5.3.1.1 Symmetric Similarity Measures; 5.3.1.2 Directional Similarity Measures; 5.3.2 Representative Vocal Features; 5.3.3 Vocal Entrainment Measures in Dialogs; 5.4 Analysis of Vocal Entrainment Measures; 5.4.1 Natural Cohesiveness of Dialogs; 5.4.2 Entrainment in Affective Interactions; 5.5 Affect Classification using Entrainment Measures; 5.5.1 Classification Framework; 5.5.1.1 Factorial Hidden Markov Model; 5.5.2 Classification Setup; 5.5.3 Classification Results and Discussions; 5.6 Conclusions and Future Work.
- Chapter 6
- Analysis of Vocal Synchrony in Couples Therapy.