“Approaches to convert RNNLM to BNLM”版本间的差异
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* VARIATIONAL APPROXIMATION OF LONG-SPAN LANGUAGE MODELS FOR LVCSR[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5947612&tag=1] | * VARIATIONAL APPROXIMATION OF LONG-SPAN LANGUAGE MODELS FOR LVCSR[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5947612&tag=1] | ||
* Conversion of Recurrent Neural Network Language Models to Weighted Finite State Transducers for Automatic Speech Recognition[http://people.irisa.fr/Gwenole.Lecorve/pdf/lecorve12c.pdf] | * Conversion of Recurrent Neural Network Language Models to Weighted Finite State Transducers for Automatic Speech Recognition[http://people.irisa.fr/Gwenole.Lecorve/pdf/lecorve12c.pdf] | ||
| − | * Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition[http://delivery.acm.org/10.1145/2590000/2583722/06645438. | + | * Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition[http://delivery.acm.org/10.1145/2590000/2583722/06645438.pdf] |
2014年11月2日 (日) 07:40的版本
main paper
comparing approaches to convert recurrent neural networks into backoff language models for efficient decoding[1]
- VARIATIONAL APPROXIMATION OF LONG-SPAN LANGUAGE MODELS FOR LVCSR[2]
- Conversion of Recurrent Neural Network Language Models to Weighted Finite State Transducers for Automatic Speech Recognition[3]
- Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition[4]
