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.

翻译：RNN-T模型广泛用于自动语音识别（ASR），其依赖RNN-T损失实现输入音频与目标序列之间的长度对齐。然而，RNN-T损失的实现复杂度以及基于对齐的优化目标分别导致了计算冗余和预测网络作用减弱。本文提出一种名为CIF-换能器（CIF-T）的新型模型，该模型将连续积分-发射（CIF）机制与RNN-T模型相结合，以实现高效对齐。通过这种方式，RNN-T损失被摒弃，从而降低了计算量，并使预测网络发挥更重要的作用。我们还引入了漏斗CIF、上下文模块、统一门控与双线性池化联合网络以及辅助训练策略，以进一步提升性能。在178小时AISHELL-1和10000小时WenetSpeech数据集上的实验表明，与RNN-T模型相比，CIF-T在降低计算开销的同时取得了最先进的结果。