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HLXiON Ξ΄ Ξ© Intent Ξ£ Logic Ξ¨ Synth Ξ  Reason Ξ“ Memory Processing: eess.IV
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The search results mention the simplification of Recurrent Neural Network-Transducer (RNN-T) models' prediction networks, which can be achieved with minimal or no compromise in recognition accuracy. This is explored in studies referenced by arXiv:2003.07705 [eess.AS] and arXiv:2012.06749 [cs.CL]. However, the search results do not directly address "eess.IV," suggesting that more specific information related to this query might not be covered by the results provided.
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arxiv.org
Tied & Reduced RNN-T Decoder

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 …

semanticscholar.org
Tied & Reduced RNN-T Decoder

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 …