Implementation of "Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning". INTERSPEECH'2022

https://arxiv.org/abs/2206.07229

Ubuntu 18.04.5 LTS

• GPU: Quadro RTX 6000
• Driver version: 450.80.02
• CUDA version: 11.0

Python 3.5

• tensorflow-gpu 2.0.0b1 (cudnn=7.6.0)
• scipy
• pandas
• matplotlib
• librosa

For example,

conda create -n strengthnet python=3.5
conda activate strengthnet
pip install -r requirements.txt
conda install cudnn=7.6.0

1. Run python utils.py to extract .wav to .h5;

2. Run python train.py to train a CNN-BLSTM based StrengthNet;

1. Put the waveforms you wish to evaluate in a folder. For example, <path>/<to>/<samples>

2. Run python test.py --rootdir <path>/<to>/<samples>

This script will evaluate all the .wav files in <path>/<to>/<samples>, and write the results to <path>/<to>/<samples>/StrengthNet_result_raw.txt.

By default, the output/strengthnet.h5 pretrained model is used.

If you find this work useful in your research, please consider citing:

@inproceedings{liu22i_interspeech,
  author={Rui Liu and Berrak Sisman and Björn Schuller and Guanglai Gao and Haizhou Li},
  title={{Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={5493--5497},
  doi={10.21437/Interspeech.2022-534}
}

The ESD corpus is released by the HLT lab, NUS, Singapore.

The strength scores for the English samples of the ESD corpus are available here.

MOSNet: https://github.com/lochenchou/MOSNet

Relative Attributes: Relative Attributes

This work is released under MIT License (see LICENSE file for details).