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tess-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

zubiiabbasi / speech-emotion-gradcam

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TESS speech emotion recognition with log-mel CNNs, speaker-independent train (OAF) / test (YAF), SpecAugment-style augmentation, Grad-CAM explainability, and a small Whisper+TF‑IDF text baseline showing emotion isn’t in transcripts alone.

machine-learning computer-vision deep-learning tensorflow grad-cam artificial-intelligence tf-idf convolutional-neural-networks audio-classification librosa data-augmentation ccn speech-emotion-recognition explainable-ai mel-spectrogram tess-dataset speaker-independent-evaluation
  • Updated May 10, 2026
  • Jupyter Notebook

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