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Speech Recognition¶


Here we include the list of available datasets as well as important publications on speech recognition via artificial neural networks.


Datasets¶


Switchboard hub 500
LibriSpeech

Publications¶


Architecture:ContextNet
Year:2020
Paper:Park, Daniel S., et al. “Improved Noisy Student Training for Automatic Speech Recognition.” arXiv preprint arXiv:2005.09629 (2020).
Architecture:GatedConvNet
Year:2018
Paper:Liptchinsky, Vitaliy, Gabriel Synnaeve, and Ronan Collobert. “Letter-based speech recognition with gated convnets.” CoRR, vol. abs/1712.09444 1 (2017).
Architecture:Deep Speech 2
Year:2016
Paper:Amodei, Dario, et al. “Deep speech 2: End-to-end speech recognition in english and mandarin.” International conference on machine learning. 2016.
Architecture:BRNN + Attention
Year:2015
Paper:Chorowski, Jan K., et al. “Attention-based models for speech recognition.” Advances in neural information processing systems. 2015.
Architecture:IBM2015
Year:2015
Paper:Saon, George, et al. “The IBM 2015 English conversational telephone speech recognition system.” arXiv preprint arXiv:1505.05899 (2015).
Architecture:Deep Speech
Year:2014
Paper:Hannun, Awni, et al. “Deep speech: Scaling up end-to-end speech recognition.” arXiv preprint arXiv:1412.5567 (2014).
Architecture:DNN + Dropout
Year:2014
Paper:Maas, Andrew L., et al. “Building DNN acoustic models for large vocabulary speech recognition.” Computer Speech & Language 41 (2017): 195-213.
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