Tied & Reduced RNN-T Decoder

Understand

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 context size of previous labels and/or using a simpler architecture for its layers instead of LSTMs.
• The benefits of such changes include reduction in model size, faster inference and power savings, which are all useful for on-device applications.
• In this work, we study ways to make the RNN-T decoder (prediction network + joint network) smaller and faster without degradation in recognition performance.