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crema-d-dataset

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souradeepdutta / Speech-Emotion-Recognition-with-CNN-LSTM-CLSTM

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his is a Speech Emotion Recognition system that classifies emotions from speech samples using deep learning models. The project uses four datasets: CREMAD, RAVDESS, SAVEE, and TESS. The model achieves an accuracy of 96% by combining CNN, LSTM, and CLSTM architectures, along with data augmentation techniques and feature extraction methods.

machine-learning deep-learning cnn lstm human-computer-interaction convolutional-lstm clstm speech-emotion-recognition ravdess-dataset speech-emotion-classification tess-dataset savee-dataset crema-d-dataset
  • Updated Nov 22, 2024
  • Jupyter Notebook

NoumanAfzal54 / Depressiondetectionsystem

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This project detects depression using audio and visual features from video input. It extracts MFCC features from the full audio and selects 20 evenly spaced frames from the video. These are fused and passed into a DenseNet201 model trained on the DAIC-WOZ dataset. Includes a Gradio web interface, deployable via Hugging Face Spaces and Google Colab.

ai ml neural-networks deeplearning mental-health gradio depression-detection daic-woz crema-d-dataset
  • Updated Jul 16, 2025
  • Jupyter Notebook

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