On-device Domain Adaptation for Noise-Robust Keyword Spotting

Introduction

On-device Domain Adaptation (ODDA) for Noise-Robust Keyword Spotting is a methodology aimed at increasing the robustness to unseen noises for a keyword spotting system. The objective of keyword spotting (KWS) is to detect a set of predefined keywords within a stream of user utterances. The difficulty of the task increases in real environments with significant noise. To improve the performance of a KWS system in noise conditions unseen during training, we propose a methodology for tailoring a model to on-site noises through ODDA.

If you use our methodology in an academic context, please cite the following publication:

Paper: Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting

@INPROCEEDINGS{cioflan2022oddaAICAS,
  author={Cioflan, Cristian and Cavigelli, Lukas and Rusci, Manuele and De Prado, Miguel and Benini, Luca},
  booktitle={2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS)}, 
  title={Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting}, 
  year={2022},
  volume={},
  number={},
  pages={82-85},
  doi={10.1109/AICAS54282.2022.9869990}}

Project structure

The project's structure is the following:

.
└── odda-for-kws/
    ├── dataset.py
    ├── environment.yml
    ├── LICENSE
    ├── main.py
    ├── model.py
    ├── README.md
    ├── train.py
    └── utils.py
    

Installation

To install the packages required to run the model's training and adaptation (in PyTorch), a conda environment can be created from environment.yml by running:

conda env create -f environment.yml

Example

To change the preprocessing parameters (e.g., number of MFCCs) or the training parameters (e.g., noise factor, learning mode -nlkws, nakws, odda-), adapt utils.py according to your settings.

To run the main script, use the command:

python main.py

Contributor

Cristian Cioflan, ETH Zurich, cioflanc@iis.ee.ethz.ch

License

The code is released under Apache 2.0, see the LICENSE file in the root of this repository for details.