QAIST

Model Architecture

• Two independent speech-emotion prediction models are used.
• They act as independent classifiers.
• Then, by ensemble method, we combine the predictions and output the final prediction.

Code

• Ensemble.ipynb:
  • code for executing ensemble function
  • receives prediction csv files from each model (each row = probs for each emotion for that audio file)
  • computes ensemble by selecting from different ensemble functions.
  • outputs csv file to submit to Eval AI. (but need to manually add column names: fileID, Emotion afterwards)
• speech_emotion_recognition_XJHe:
  • this code is implemented based on https://ieeexplore.ieee.org/document/8421023 this paper.
  • to execute, run train.ipynb and execute test.ipynb (be careful about the path of train and val data in extract_mel.py)
  • Dependencies
    • tensorflow == 1.5.0
    • sklearn
    • matplotlib
    • python_speech_features
    • wave