Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognition

Dimensions overall score 7.0

• The proposed human-machine collaborative framework achieves up to 86.59% accuracy on Vietnamese speech emotion recognition.result 95%
  Evidencepartial

  The proposed method achieves strong performance, reaching up to 86.59% accuracy

  Implicationpartial

  Explicitly stated in abstract with specific numeric result

  Verificationpartial

  partial

• The framework uses a confidence-based routing mechanism to distinguish between easy and ambiguous speech samples.method 90%
  Evidencepartial

  A confidence-based routing mechanism is introduced to distinguish between easy and ambiguous samples

  Implicationpartial

  Directly stated in abstract as a core component of the method

  Verificationpartial

  partial

• The method achieves Macro F1 scores around 0.85-0.86 on Vietnamese speech emotion recognition.result 90%
  Evidencepartial

  Macro F1 around 0.85-0.86, demonstrating its effectiveness

  Implicationpartial

  Explicitly stated in abstract with specific numeric range

  Verificationpartial

  partial

• Vietnamese Speech Emotion Recognition is challenging due to ambiguous acoustic patterns and lack of reliable annotated data.technical 85%
  Evidencepartial

  Vietnamese Speech Emotion Recognition (SER) remains challenging due to ambiguous acoustic patterns and the lack of reliable annotated data

  Implicationpartial

  Directly stated as problem motivation in abstract

  Verificationpartial

  partial

• The framework uses LLM-based reasoning guided by structured rules derived from human annotation behavior.method 85%
  Evidencepartial

  LLM-based reasoning, where acoustic feature-based models are used to provide auxiliary signals

  Implicationpartial

  Directly stated as core method component in abstract

  Verificationpartial

  partial

• Experiments were conducted on a Vietnamese speech dataset of 2,764 samples across three emotion classes.method 90%
  Evidencepartial

  Experiments are conducted on a Vietnamese speech dataset of 2,764 samples across three emotion classes

  Implicationpartial

  Explicitly stated with specific dataset details

  Verificationpartial

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• The dataset has high inter-annotator agreement with Fleiss Kappa = 0.8574.method 90%
  Evidencepartial

  with high inter-annotator agreement (Fleiss Kappa = 0.8574), ensuring reliable ground truth

  Implicationpartial

  Explicitly stated with specific statistical measure

  Verificationpartial

  partial

• The framework employs an iterative refinement strategy to continuously improve performance through error analysis.method 85%
  Evidencepartial

  an iterative refinement strategy is employed to continuously improve system performance through error analysis and rule updates

  Implicationpartial

  Directly stated as part of the method description

  Verificationpartial

  partial

Competitive landscape

A human-AI collaborative framework for Vietnamese Speech Emotion Recognition that uses LLMs to reason on ambiguous cases, improving accuracy in low-resource settings.