We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users during inference. Our method incorporates a gating mechanism and LID loss, enabling transformer experts to learn language-specific information. By combining gated transformer experts with shared transformer layers, we construct multilingual transformer blocks and utilize linear experts to effectively regularize the joint network. The curriculum training scheme leverages LID to guide the gated experts in improving their respective language performance. Experimental results on a bilingual task involving English and Spanish demonstrate significant improvements, with average relative word error reductions of 12.5% and 7.3% compared to the baseline bilingual and monolingual models, respectively. Notably, our method achieves performance comparable to the upper-bound model trained and inferred with oracle LID. Extending our approach to trilingual, quadrilingual, and pentalingual models reveals similar advantages to those observed in the bilingual models, highlighting its ease of extension to multiple languages.

翻译：我们提出门控语言专家与课程训练方法，用于增强多语言Transformer-Transducer模型，且推理过程中无需用户提供语言识别（LID）输入。该方法通过引入门控机制和LID损失函数，使Transformer专家能够学习语言特定信息。通过将门控Transformer专家与共享Transformer层相结合，我们构建了多语言Transformer模块，并利用线性专家有效正则化联合网络。课程训练方案借助LID引导门控专家提升各自语言的性能。在英语和西班牙语的双语任务实验中，该方法相比基线双语模型和单语模型分别实现了12.5%和7.3%的平均相对词错误率下降。值得注意的是，我们的方法达到了与使用真实LID训练并推理的上界模型相当的性能。将本方法扩展至三语、四语和五语模型时，观察到了与双语模型类似的优势，凸显了其易于扩展至多语言场景的特性。