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    Area of Science:

    • Speech Emotion Recognition
    • Computational Linguistics
    • Affective Computing

    Background:

    • Accurate emotion detection from audio is crucial for mental health research.
    • Audio-based emotion recognition is a significant area of study.
    • Existing methods require improvement for reliable emotion classification.

    Purpose of the Study:

    • To propose a novel two-level, multi-way classifier for classifying seven emotions from the Emo-DB database.
    • To enhance the accuracy of speech emotion recognition.
    • To improve upon state-of-the-art performance in audio-based emotion detection.

    Main Methods:

    • A two-level, multi-way classifier was developed.
    • A random forest classifier was employed with state-of-the-art features for affective speech analysis.
    • A confusion matrix analysis guided the construction of second-level classifiers for confused emotion pairs.

    Main Results:

    • The proposed classifier achieved performance of 73.3% on the training set and 72.9% on a non-intersecting set.
    • This performance surpasses current state-of-the-art results in emotion detection.
    • Analysis considered emotion confusion within Russel's circumplex model to explain improvements.

    Conclusions:

    • The developed two-level classifier significantly improves speech emotion recognition accuracy.
    • This approach offers a promising advancement for mental health applications relying on audio emotion analysis.
    • Further research can explore the model's effectiveness across different emotional datasets and cultural contexts.