Abstract: Emotion recognition from human voice has emerged as a crucial technology in various fields, including healthcare, human-computer interaction, and artificial intelligence-based applications. The ability to detect emotions based on speech signals enhances system adaptability and improves user experience. This study presents a progressive implementation of an emotion detection system that integrates Natural Language Processing (NLP) and speech feature extraction techniques. The system utilizes machine learning and deep learning models to classify emotions, including happiness, sadness, anger, and fear, based on vocal expressions. The approach involves extracting speech parameters such as pitch, tone, energy, and amplitude, which are analyzed using ML-based classifiers. Additionally, NLP techniques, including text sentiment analysis and word embedding’s, enhance classification accuracy by providing contextual insights. The system is implemented on Raspberry Pi hardware, making it portable and scalable for real-world applications. Initial findings indicate that deep learning models outperform traditional ML approaches, offering improved accuracy. Future advancements will focus on reducing background noise, optimizing feature selection, and incorporating real-time emotion tracking.
Keyword-Speech Emotion Recognition, NLP, Machine Learning, Deep Learning, Speech Processing, Human-Computer Interaction.
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DOI:
10.17148/IJARCCE.2025.14373
