emotion-recognition-using-speech

Speech Emotion Recognition Introduction This repository handles building and training Speech Emotion Recognition System. The basic idea behind this tool is to build and train/test a suited machine learning ( as well as deep learning ) algorithm that could recognize and detects human emotions from speech. This is useful for many industry fields such as making product recommendations, affective computing, etc. Check this tutorial for more information. Requirements Python 3.6+ Python Packages tensorflow librosa==0.6.3 numpy pandas soundfile==0.9.0 wave scikit-learn==0.24.2 tqdm==4.28.1 matplotlib==2.2.3 pyaudio==0.2.11 ffmpeg (optional): used if you want to add more sample audio by converting to 16000Hz sample rate and mono channel which is provided in convertwavs.py Install these libraries by the following command: Dataset This repository used 4 datasets (including this repo's custom dataset) which are downloaded and formatted already in data folder: RAVDESS : The Ryson Audio-Visual Database of Emotional Speech and Song that contains 24 actors (12 male, 12 female), vocalizing two lexically-matched statements in a neutral North American accent.