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iON AI
Synthesis
The search results indicate that research on Recurrent Neural Network-Transducer (RNN-T) models suggests that the prediction network can be simplified under certain conditions without significantly affecting recognition accuracy. This simplification involves reducing the complexity of the network, as demonstrated in studies like arXiv:2003.07705 [eess.AS] and arXiv:2012.06749 [cs.CL]. These findings highlight the potential for more efficient RNN-T models that maintain performance while reducing computational demands.
β Top Result
Previous works on the Recurrent Neural Network-Transducer (RNN-T) models have shown that, under some conditions, it is possible to simplify its prediction network with little or no loss in recognition accuracy (arXiv:2003.07705 [eess.AS], [2], arXiv:2012.06749 [cs.CL]). This is done by limiting the β¦
Previous works on the Recurrent Neural Network-Transducer (RNN-T) models have shown that, under some conditions, it is possible to simplify its prediction network with little or no loss in recognition accuracy (arXiv:2003.07705 [eess.AS], [2], arXiv:2012.06749 [cs.CL]). This is done by limiting the β¦