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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Assessing random forest performance in low resource speech emotion recognition.

Muhammad Adeel1,2,3, Zhi-Yong Tao4,5, Shu-Ya Jin4,5

  • 1Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology, Guilin, 541004, People's Republic of China. adeel.muhammad@guet.edu.cn.

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Summary

This study shows a random forest classifier achieves 94.53% accuracy for Urdu speech emotion recognition (SER), identifying happiness, sadness, and anger. This advances empathetic AI for low-resource languages.

Keywords:
Mel frequency cepstral coefficientsRandom forest classifierSpeech emotion recognitionUrdu low-resource speech analysis

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Area of Science:

  • Human-Computer Interaction (HCI)
  • Artificial Intelligence (AI)
  • Speech Processing

Background:

  • Speech Emotion Recognition (SER) is crucial for empathetic AI in HCI.
  • Urdu is a low-resource language with limited SER research.
  • Existing SER models often struggle with linguistic diversity.

Purpose of the Study:

  • To evaluate the effectiveness of a Random Forest (RF) classifier for SER in Urdu.
  • To identify key Mel-frequency cepstral coefficients (MFCCs) for emotion discrimination.
  • To advance AI's emotional understanding in under-resourced languages.

Main Methods:

  • Utilized Mel-frequency cepstral coefficients (MFCCs) for feature extraction.
  • Employed a Random Forest (RF) classifier for emotion classification.
  • Focused on three primary emotions: happiness, sadness, and anger.

Main Results:

  • Achieved a validation accuracy of 94.53% for Urdu SER.
  • Demonstrated the robustness of the RF classifier for this task.
  • Identified significant MFCC features contributing to emotion differentiation.

Conclusions:

  • The RF classifier is a viable and effective tool for SER in low-resource languages like Urdu.
  • This research paves the way for more emotionally intelligent AI systems.
  • Future work includes expanding emotion categories and exploring diverse datasets.