RNN-T models are widely used in ASR, which rely on the RNN-T loss to achieve length alignment between input audio and target sequence. However, the implementation complexity and the alignment-based optimization target of RNN-T loss lead to computational redundancy and a reduced role for predictor network, respectively. In this paper, we propose a novel model named CIF-Transducer (CIF-T) which incorporates the Continuous Integrate-and-Fire (CIF) mechanism with the RNN-T model to achieve efficient alignment. In this way, the RNN-T loss is abandoned, thus bringing a computational reduction and allowing the predictor network a more significant role. We also introduce Funnel-CIF, Context Blocks, Unified Gating and Bilinear Pooling joint network, and auxiliary training strategy to further improve performance. Experiments on the 178-hour AISHELL-1 and 10000-hour WenetSpeech datasets show that CIF-T achieves state-of-the-art results with lower computational overhead compared to RNN-T models.

该篇论文介绍了一种名为CIF-Transducer (CIF-T)的模型，它将连续积分-放电（CIF）机制与RNN-T模型结合起来，实现了高效的对齐。通过舍弃RNN-T损失函数，该模型减少了计算复杂性，并使预测网络的作用更加显著。同时，引入了Funnel-CIF、Context Blocks、统一门控和双线性池化联合网络以及辅助训练策略来进一步提高性能。在AISHELL-1和WenetSpeech数据集的实验中，CIF-T相对于RNN-T模型，在更小的计算开销下取得了最先进的结果。

告别RNN-T Loss：一种新颖的基于CIF的自动语音识别Transducer架构