Speech-Emotion-Analyzer and speech-emotion-recognition
About Speech-Emotion-Analyzer
MiteshPuthran/Speech-Emotion-Analyzer
The neural network model is capable of detecting five different male/female emotions from audio speeches. (Deep Learning, NLP, Python)
This project helps businesses understand customer sentiment during calls or interactions. It takes audio speech as input and tells you if the speaker (male or female) is angry, calm, fearful, happy, or sad. Call center managers, marketers, or even product developers could use this to gauge emotional responses.
About speech-emotion-recognition
benkhelifamohamedtaher/speech-emotion-recognition
Deep learning system for emotion recognition from speech, achieving 50.5% accuracy on 8-class classification using transformer architecture and real-time analysis
This project helps systems understand human emotions by analyzing spoken words. It takes audio input, like a live conversation or a recording, and identifies one of eight emotions (e.g., anger, happiness, sadness). This is useful for anyone building or managing virtual assistants, mental health support tools, or customer service platforms that need to respond appropriately to user feelings.
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