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.
133 stars. No commits in the last 6 months.
Use this if you need to automatically identify the emotional tone of speech in audio files.
Not ideal if you require very high accuracy for critical decision-making, as the reported accuracy is 45%.
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133
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License
GPL-3.0
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Last pushed
Dec 02, 2020
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