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Speech Collage Dataset (Sample)
This is a sample of the Speech Collage Dataset, generated and published by Amir Hussein, PhD Student,
Johns Hopkins University, 2024.
Description
The Speech Collage Dataset consists of code-switched audio generated from publicly available monolingual corpora. The dataset is designed to facilitate research in automatic speech recognition (ASR), multilingual processing, and data augmentation techniques for code-switching scenarios.
This repository provides a sample subset of the full dataset for research and evaluation purposes.
The dataset was introduced in the following publication:
Amir Hussein, Dorsa Zeinali, Ondřej Klejch, Matthew Wiesner, Brian Yan, Shammur Chowdhury, Ahmed Ali, Shinji Watanabe, and Sanjeev Khudanpur.
"Speech Collage: Code-Switched Audio Generation by Collaging Monolingual Corpora."
In ICASSP 2024 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 12006-12010, 2024.
DOI: 10.1109/ICASSP48485.2024.10446857
License
This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivs 3.0 (CC BY-NC-ND 3.0).
You may use and share this dataset for non-commercial research purposes, but:
- No modifications or derivative works are allowed.
- Commercial use is prohibited.
- Attribution to the original authors must be provided.
For details, see: Creative Commons BY-NC-ND 3.0 License.
Citation
If you use this dataset in research, please cite the following paper:
@INPROCEEDINGS{10446857,
author={Hussein, Amir and Zeinali, Dorsa and Klejch, Ondřej and Wiesner, Matthew and Yan, Brian and Chowdhury, Shammur and Ali, Ahmed and Watanabe, Shinji and Khudanpur, Sanjeev},
booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Speech Collage: Code-Switched Audio Generation by Collaging Monolingual Corpora},
year={2024},
pages={12006-12010},
keywords={Training;Speech coding;Zero-shot learning;Splicing;Signal processing;Data augmentation;Data models;Code-switching;ASR;data augmentation;end-to-end;zero-shot learning},
doi={10.1109/ICASSP48485.2024.10446857}
}
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