Identifying emotion from speech is a non-trivial task pertaining to the ambiguous definition of emotion itself. In this work, we build light-weight multimodal machine learning models and compare it against the heavier and less interpretable deep learning counterparts. For both types of models, we use hand-crafted features from a given audio signal. Our experiments show that the light-weight models are comparable to the deep learning baselines and even outperform them in some cases, achieving state-of-the-art performance on the IEMOCAP dataset.

The hand-crafted feature vectors obtained are used to train two types of models:

1. ML-based: Logistic Regression, SVMs, Random Forest, eXtreme Gradient Boosting and Multinomial Naive-Bayes.
2. DL-based: Multi-Layer Perceptron, LSTM Classifier

This project was carried as a course project for the course CS 698 - Computational Audio taught by Prof. Richard Mann at the University of Waterloo. For a more detailed explanation, please check the report.

The IEMOCAP dataset was used for all the experiments in this work. Please refer to the report for a detailed explanation of pre-processing steps applied to the dataset.

All the experiments have been tested using the following libraries:

• xgboost==0.82
• torch==1.0.1.post2
• scikit-learn==0.20.3
• numpy==1.16.2
• jupyter==1.0.0
• pandas==0.24.1
• librosa==0.7.0

To avoid conflicts, it is recommended to setup a new python virtual environment to install these libraries. Once the env is setup, run pip install -r requirements.txt to install the dependencies.

1. Clone this repository by running git clone [email protected]:Demfier/multimodal-speech-emotion-recognition.
2. Go to the root directory of this project by running cd multimodal-speech-emotion-recognition/ in your terminal.
3. Start a jupyter notebook by running jupyter notebook from the root of this project.
4. Run 1_extract_emotion_labels.ipynb to extract labels from transriptions and compile other required data into a csv.
5. Run 2_build_audio_vectors.ipynb to build vectors from the original wav files and save into a pickle file
6. Run 3_extract_audio_features.ipynb to extract 8-dimensional audio feature vectors for the audio vectors
7. Run 4_prepare_data.ipynb to preprocess and prepare audio + video data for experiments
8. It is recommended to train LSTMClassifier before running any other experiments for easy comparsion with other models later on:

• Change config.py for any of the experiment settings. For instance, if you want to train a speech2emotion classifier, make necessary changes to lstm_classifier/s2e/config.py. Similar procedure follows for training text2emotion (t2e) and text+speech2emotion (combined) classifiers.
• Run python lstm_classifier.py from lstm_classifier/{exp_mode} to train an LSTM classifier for the respective experiment mode (possible values of exp_mode: s2e/t2e/combined)

9. Run 5_audio_classification.ipynb to train ML classifiers for audio
10. Run 5.1_sentence_classification.ipynb to train ML classifiers for text
11. Run 5.2_combined_classification.ipynb to train ML classifiers for audio+text

Note: Make sure to include correct model paths in the notebooks as not everything is relative right now and it needs some refactoring

UPDATE: You can access the preprocessed data files here to skip the steps 4-7: https://www.dropbox.com/scl/fo/jdzz2y9nngw9rxsbz9vyj/h?rlkey=bji7zcqclusagzfwa7alm59hx&dl=0

Accuracy, F-score, Precision and Recall has been reported for the different experiments.

Audio

E1: Ensemble (RF + XGB + MLP)

Text

E2: Ensemble (RF + XGB + MLP + MNB + LR) E1: Ensemble (RF + XGB + MLP)

Audio + Text

For more details, please refer to the report

If you find this work useful, please cite:

@article{sahu2019multimodal,
  title={Multimodal Speech Emotion Recognition and Ambiguity Resolution},
  author={Sahu, Gaurav},
  journal={arXiv preprint arXiv:1904.06022},
  year={2019}
}