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We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments.
A tutorion on hidden Markov models and selected applications in speech recognition
Rabiner, Lawrence R · 1989
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A comparison of approaches to on-line handwritten character recognition
Kassel, Robert H · 1995
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Text chunking using transformation-based learning
Ramshaw, Lance A. and Marcus, Mitchell P · 1995
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Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
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Semi-Markov conditional random fields for information extraction
Sarawagi, Sunita and Cohen, William W · 2004
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Max-margin Markov networks
Taskar, Ben, Guestrin, Carlos, and Koller, Daphne · 2004
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Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, Alex and Schmidhuber, Jürgen · 2005
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Kingma, Diederik and Ba, Jimmy · 2014
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End-to-end attention-based large vocabulary speech recognition
Bahdanau, Dzmitry, Chorowski, Jan, Serdyuk, Dmitriy, Brakel, Philémon, and Bengio, Yoshua · 2015
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Chan, William, Jaitly, Navdeep, Le, Quoc V., and Vinyals, Oriol · 2015
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Deep speech: Scaling up end-to-end speech recognition
Hannun, Awni Y., Case, Carl, Casper, Jared, Catanzaro, Bryan C., Diamos, Greg, Elsen, Erich, Prenger, Ryan, Satheesh, Sanjeev, Sengupta, Shubho, Coates, Adam, and Ng, Andrew Y · 2014
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Ling, Wang, Dyer, Chris, Black, Alan W, and Trancoso, Isabel · 2015
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Lexicon-free conversational speech recognition with neural networks
Maas, Andrew L., Xie, Ziang, Jurafsky, Dan, and Ng, Andrew Y · 2015
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