We propose gated language experts to improve multilingual transformer
transducer models without any language identification (LID) input from users
during inference. We define gating mechanism and LID loss to let transformer
encoders learn language-dependent information, construct the multilingual
transformer block with gated transformer experts and shared transformer layers
for compact models, and apply linear experts on joint network output to better
regularize speech acoustic and token label joint information. Furthermore, a
curriculum training scheme is proposed to let LID guide the gated language
experts for better serving their corresponding languages. Evaluated on the
English and Spanish bilingual task, our methods achieve average 12.5% and 7.3%
relative word error reductions over the baseline bilingual model and
monolingual models, respectively, obtaining similar results to the upper bound
model trained and inferred with oracle LID. We further explore our method on
trilingual, quadrilingual, and pentalingual models, and observe similar
advantages as in the bilingual models, which demonstrates the easy extension to
more languages.

使用门控语言专家和课程训练方案改善多语种 Transformer 转导模型的性能，并在英语和西班牙语的双语任务中比基准双语和单语模型分别获得 12.5% 和 7.3% 的相对字错误率降低，并且扩展到更多语言也具有类似的优势。