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Related Experiment Video

Updated: Jul 23, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Diversifying Emotional Dialogue Generation via Selective Adversarial Training.

Bo Li1, Huan Zhao1, Zixing Zhang1

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.

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|July 14, 2023
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Summary

This study introduces a novel method for generating diverse emotional responses in conversational AI by using selective perturbation. The approach enhances emotional expression and response variety, addressing limitations in current human-like AI systems.

Keywords:
conditional variational autoencoderdialog systemsdiversity enhancementemotional response generationlatent variables

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Human-like conversational AI requires sophisticated emotional perception and expression.
  • Current deep neural approaches for conversation generation have limitations in controlling emotional responses and often produce generic, high-frequency outputs.

Purpose of the Study:

  • To develop a method for generating diverse and emotionally appropriate responses in conversational AI.
  • To address the challenge of generic responses in AI-generated conversations.

Main Methods:

  • A novel model employing selective word perturbation to enhance response diversity.
  • A global emotion control module to maintain response coherence and prevent emotional/semantic drift.

Main Results:

  • The proposed model significantly improves emotional expression in AI-generated responses.
  • Experimental results demonstrate enhanced response diversity compared to existing baseline methods.

Conclusions:

  • Selective perturbation combined with global emotion control is effective for generating diverse and emotionally nuanced AI responses.
  • This method advances the development of more human-like and engaging conversational AI systems.