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Building speech recognizers in multiple languages typically involves replicating a monolingual training recipe for each language, or utilizing a multi-task learning approach where models for different languages have separate output labels but share some internal parameters.
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Sepp Hochreiter and Jürgen Schmidhuber, · 1997
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“Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,”
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber, · 2006
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“A study on multilingual acoustic modeling for large vocabulary asr,”
Hui Lin, Li Deng, Dong Yu, Yi-fan Gong, Alex Acero, and Chin-Hui Lee, · 2009
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“Investigation on cross-and multilingual mlp features under matched and mismatched acoustical conditions,”
Zoltán Tüske, Joel Pinto, Daniel Willett, and Ralf Schlüter, · 2013
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“Multilingual training of deep neural networks,”
Arnab Ghoshal, Pawel Swietojanski, and Steve Renals, · 2013
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“Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers,”
Jui-Ting Huang, Jinyu Li, Dong Yu, Li Deng, and Yifan Gong, · 2013
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“Multilingual acoustic models using distributed deep neural networks,”
Georg Heigold, Vincent Vanhoucke, Alan Senior, Patrick Nguyen, M Ranzato, Matthieu Devin, and Jeffrey Dean, · 2013
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“Multilingual multilayer perceptron for rapid language adaptation between and across language families.,”
Ngoc Thang Vu and Tanja Schultz, · 2013
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“Hybrid speech recognition with deep bidirectional lstm,”
Alex Graves, Navdeep Jaitly, and Abdel-rahman Mohamed, · 2013
Cited alongside, same era.
“Improving language-universal feature extraction with deep maxout and convolutional neural networks,”
Yajie Miao and Florian Metze, · 2014
Cited alongside, same era.
“Automatic speech recognition for under-resourced languages: A survey,”
Laurent Besacier, Etienne Barnard, Alexey Karpov, and Tanja Schultz, · 2014
Cited alongside, same era.
“Towards end-to-end speech recognition with recurrent neural networks,”
Alex Graves and Navdeep Jaitly, · 2014
Cited alongside, same era.
“Deep speech: Scaling up end-to-end speech recognition,”
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al., · 2014
Cited alongside, same era.
“Transfer learning for speech and language processing,”
Dong Wang and Thomas Fang Zheng, · 2015
Later among the works it cites.
“EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding,”
Yajie Miao, Mohammad Gowayyed, and Florian Metze, · 2015
Later among the works it cites.
“Attention-based models for speech recognition,”
Jan K Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio, · 2015
Later among the works it cites.
William Chan, Navdeep Jaitly, Quoc V Le, and Oriol Vinyals, · 2015
Later among the works it cites.
“Fast and accurate recurrent neural network acoustic models for speech recognition,”
Haşim Sak, Andrew Senior, Kanishka Rao, and Françoise Beaufays, · 2015
Later among the works it cites.
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Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, · 2014
Cited alongside, same era.
“End-to-end continuous speech recognition using attention-based recurrent NN: First results,”
Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, · 2014
Cited alongside, same era.
“Unicode-based graphemic systems for limited resource languages,”
Mark JF Gales, Kate M Knill, and Anton Ragni, · 2015
Cited alongside, same era.
“Advances in all-neural speech recognition,”
Geoffrey Zweig, Chengzhu Yu, Jasha Droppo, and Andreas Stolcke, · 2017
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“Joint ctc-attention based end-to-end speech recognition using multi-task learning,”
Suyoun Kim, Takaaki Hori, and Shinji Watanabe, · 2017
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“Transfer learning for speech recognition on a budget,”
Julius Kunze, Louis Kirsch, Ilia Kurenkov, Andreas Krug, Jens Johannsmeier, and Sebastian Stober, · 2017
Closest in time.