“Approaches to convert RNNLM to BNLM”版本间的差异
来自cslt Wiki
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* 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.pdf?ip=166.111.134.19&id=2583722&acc=ACTIVE%20SERVICE&key=BF85BBA5741FDC6E%2E587F3204F5B62A59%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=593700348&CFTOKEN=42817565&__acm__=1414915690_c0bb48e7020d821b2f5841fa71632ebb] | * 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?ip=166.111.134.19&id=2583722&acc=ACTIVE%20SERVICE&key=BF85BBA5741FDC6E%2E587F3204F5B62A59%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=593700348&CFTOKEN=42817565&__acm__=1414915690_c0bb48e7020d821b2f5841fa71632ebb] | ||
| + | =related algorithm= | ||
| + | * gibbs sampling[http://cos.name/2013/01/lda-math-mcmc-and-gibbs-sampling/] | ||
2014年11月2日 (日) 14:52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]
- gibbs sampling[5]
