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Published in International Conference on Speech and Computer (SPECOM), 2020
In this work, we designed a lightweight CNN architecture and improved its performance under noisy conditions using strong regularization (SpecAugment and DropBlock) and knowledge distillation techniques.
Recommended citation: T. Alam and A. Khan, “Lightweight CNN for Robust Voice Activity Detection,” in International Conference on Speech and Computer(SPECOM). Springer, 2020, pp. 1–12. https://link.springer.com/chapter/10.1007/978-3-030-60276-5_1
Published in ASONAM, 2020
This work consolidates disaster image classification datasets from various sources and across different tasks. Baseline results using different CNN models are also provided.
Recommended citation: F. Alam, F. Ofli, M. Imran,T. Alam, and U. Qazi, “Social Media Image Classification Benchmarks for Various Disaster Response Tasks,” in 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2020
Published in Proceedings of the 6th Workshop on Noisy User-generated Text (W-NUT 2020)@EMNLP, 2020
We used different Transformer models for the punctuation restoration task on a high (English) and low (resource) language. We also proposed a novel augmentation strategy tailored for improving performance on ASR transcriptions.
Recommended citation: T. Alam, A. Khan, and F. Alam, “Punctuation Restoration using Transformer Models for High-and Low-Resource Languages,” in Proceedings of the 6th Workshop on Noisy User-generated Text (W-NUT2020)@EMNLP. 2020. http://noisy-text.github.io/2020/pdf/2020.d200-1.18.pdf
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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