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
amanbasu/speech-emotion-recognition
Detecting emotions using MFCC features of human speech using Deep Learning
This project helps you analyze human speech to detect underlying emotions. You provide raw audio recordings, and it classifies the speaker's emotion as happy, sad, angry, frustrated, neutral, or fear. This tool is useful for anyone working with spoken interactions, such as customer service managers, qualitative researchers, or content creators.
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