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Related Experiment Video

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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Speech emotion recognition based on brain and mind emotional learning model.

Sara Motamed1, Saeed Setayeshi2, Azam Rabiee3

  • 1Department of Computer Engineering, Fouman and Shaft Branch, Islamic Azad University, Fouman, Iran.

Journal of Integrative Neuroscience
|July 17, 2018
PubMed
Summary

This study introduces a novel speech emotion recognition model inspired by brain and mind functions. The proposed system effectively identifies human emotions in speech, even under noisy conditions, enhancing human-computer interaction.

Keywords:
Emotional speechadaptive neural fuzzy inference system (ANFIS)brain and mind emotional learning (BMEL)knowledgelearning automata

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Signal Processing

Background:

  • Speech emotion recognition is crucial for natural human-computer interaction.
  • Existing models face challenges in accurately identifying emotions, especially in noisy environments.
  • Understanding the brain-mind relationship offers a new avenue for improving emotion recognition.

Purpose of the Study:

  • To propose a novel speech emotion recognition model based on brain and mind memory systems.
  • To computationally model the interaction between brain short-term memory (BSTM) and mind long-term memory (MLTM) for emotion recognition.
  • To evaluate the model's performance and robustness against noise.

Main Methods:

  • A two-part model was developed: brain short-term memory (BSTM) for initial processing and mind long-term memory (MLTM) for knowledge storage.
  • Emotional speech signals were used as input to the BSTM.
  • The model's efficiency was tested by analyzing its performance under varying noise conditions.

Main Results:

  • The proposed model demonstrated a strong capability in recognizing human emotions from speech.
  • Performance remained robust even when input signals were subjected to different levels of noise.
  • Experimental results validated the model's effectiveness compared to existing recognition methods.

Conclusions:

  • The novel BSTM-MLTM model offers a promising approach to speech emotion recognition.
  • The model's resilience to noise highlights its practical applicability in real-world scenarios.
  • This work suggests a new perspective on the brain-mind relationship in information processing.