In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models: Monotonic Chunkwise Attention (MoChA) and Recurrent Neural Network-Transducer (RNN-T). We explore three recently proposed data augmentation techniques, namely, multi-conditioned training using an acoustic simulator, Vocal Tract Length Perturbation (VTLP) for speaker variability, and SpecAugment. Experimental results show that unidirectional models are in general more sensitive to noisy examples in the training set. It is observed that the final performance of the model depends on the proportion of training examples processed by data augmentation techniques. MoChA models generally perform better than RNN-T models. However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques. On the other hand, RNN-T models perform better than MoChA models in terms of latency, inference time, and the stability of training. Additionally, RNN-T models are generally more robust against noise and reverberation. All these advantages make RNN-T models a better choice for streaming on-device speech recognition compared to MoChA models.

翻译：在本文中,我们对两种不同的在线流式语音识别模型的稳健性进行了比较研究:单向分流式注意(Mochna)和经常性神经网络传输器(RNN-T)。我们探讨了最近提出的三种数据增强技术,即:使用声学模拟器的多条件培训、用于语音变异的Vocal Tract later Proturbation(VTLP)和Speacument。实验结果显示,单向型模型一般对培训成套中吵闹的例子比较敏感。据观察,该模型的最终性能取决于数据增强技术处理的培训实例的比例。MOCHA模型一般比RNN-T模型的运行更好。然而,我们注意到,对MCHA模型的培训似乎更加敏感,如培训成套特点和纳入其他扩音技术。另一方面,在弹性、推论时间和培训稳定性方面,RNE-T模型比MA模型更可靠。此外,RNN-T模型一般比RNA模型更能比RNR-revical的磁度识别。