Score-balanced Loss for Multi-aspect Pronunciation Assessment

This repository is the implementation of the paper, Score-balanced Loss for Multi-aspect Pronunciation Assessment (Interspeech 2023).

Our code is based on the open source, https://github.com/YuanGongND/gopt (Gong et al, 2022).

Citation

Please cite our paper, if you find this repository helpful.

@inproceedings{do23b_interspeech,
  author={Heejin Do and Yunsu Kim and Gary Geunbae Lee},
  title={{Score-balanced Loss for Multi-aspect Pronunciation Assessment}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
  pages={4998--5002},
  doi={10.21437/Interspeech.2023-1679}
}

Dataset

An open source dataset, SpeechOcean762 (licenced with CC BY 4.0) is used. You can download it from https://www.openslr.org/101.

Package Requirements

Install below packages in your virtual environment before running the code.

• python version 3.8.10
• pytorch version '1.13.1+cu117'
• numpy version 1.20.3
• pandas version 1.5.0

You can run below command on your virtual environment

• pip install -r requirements.txt

Training and Evaluation (GOPT with SB Loss)

This bash script will run each model 5 times with ([0, 1, 2, 3, 4]).

• cd src
• bash run_SB_pred.sh

Note that every run does not produce the same results due to the random elements.

Run baseline (GOPT)

This bash script will run each model 5 times with ([0, 1, 2, 3, 4]).

• cd src
• bash run_gopt.sh