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Emotion recognition for human-computer interaction using high-level descriptors.

Chaitanya Singla¹, Sukhdev Singh², Preeti Sharma¹

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

Scientific Reports

|May 27, 2024

Summary

This summary is machine-generated.

This study introduces a new Deep Learning (DL) method for Speech Emotion Recognition (SER) in Punjabi, achieving 69% accuracy. The approach uses spectrograms and social media data, outperforming traditional techniques.

Keywords:

Deep learning Emotion recognition High-level features Punjabi database Punjabi speech emotion recognition Speech emotion recognition (SER)

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Area of Science:

Artificial Intelligence
Speech Processing
Machine Learning

Background:

Deep Learning (DL) is increasingly used for Speech Emotion Recognition (SER).
Focus on SER for Punjabi language speakers is growing.
Existing methods for Punjabi SER lack sufficient accuracy.

Purpose of the Study:

To develop and evaluate a novel DL-based approach for SER in the Punjabi language.
To construct and preprocess a labeled Punjabi speech corpus from social media.
To improve the accuracy of emotion recognition in Punjabi speech signals.

Main Methods:

Utilized Convolutional Neural Networks (CNNs) for SER.
Employed spectrograms as the primary feature representation.
Created a custom dataset from Punjabi films and web series, sourced from social media.

Main Results:

The proposed DL approach achieved an accuracy of 69% for Punjabi SER.
Outperformed traditional methods: Decision Trees (49%), Naïve Bayes (52%), and Random Forests (61%).
Demonstrated effective learning of discriminative patterns for emotion recognition.

Conclusions:

The novel DL method significantly improves accuracy in Punjabi Speech Emotion Recognition.
Spectrogram-based feature representation is effective for this task.
Social media is a viable source for creating speech emotion datasets.