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Related Experiment Video

Updated: May 14, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

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Published on: December 23, 2025

Human behavior state profile mapping based on recalibrated speech affective space model.

N Kamaruddin¹, A Wahab

¹Faculty of Computer and Mathematical Sciences, MARA University of Technology (UiTM), 40400 Shah Alam, Selangor, Malaysia. norhaslinda@tmsk.uitm.edu.my

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

This study links human behavior states (HBS) to emotions using speech analysis. Mapping behavior to speech reveals emotional insights crucial for understanding holistic health.

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Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

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Published on: August 9, 2024

Area of Science:

Psychology
Speech Analysis
Human-Computer Interaction

Background:

Holistic health encompasses physical, mental, and emotional well-being.
Emotional health is key to behavioral control and quality of life.
Understanding human behavior is vital for a comprehensive health perspective.

Purpose of the Study:

To map human behavior state (HBS) profiles onto a recalibrated speech affective space model (rSASM).
To explore the relationship between quantifiable speech emotion and human behavior.
To provide a novel approach for behavior analysis through emotion primitives.

Main Methods:

Developing a recalibrated speech affective space model (rSASM).
Profiling four distinct driving human behavior states (distracted, laughing, sleepy, normal) onto the rSASM.
Quantifying emotion through speech, considering its dynamic and cultural aspects.

Main Results:

Empirical results demonstrate the proposed approach's ability to complement existing behavior analysis methods.
The approach offers enhanced explanatory components from the perspective of emotion primitives (valence and arousal).
Visualizations show correlations between specific HBS and emotional states within the rSASM.

Conclusions:

Mapping HBS onto rSASM provides valuable insights into the emotional underpinnings of behavior.
This method can enhance future behavior analysis systems for improved performance.
The study highlights the interconnectedness of emotion, speech, and human behavior in the context of health.