Code-switching describes the practice of using more than one language in the
same sentence. In this study, we investigate how to optimize a neural
transducer based bilingual automatic speech recognition (ASR) model for
code-switching speech. Focusing on the scenario where the ASR model is trained
without supervised code-switching data, we found that semi-supervised training
and synthetic code-switched data can improve the bilingual ASR system on
code-switching speech. We analyze how each of the neural transducer's encoders
contributes towards code-switching performance by measuring encoder-specific
recall values, and evaluate our English/Mandarin system on the ASCEND data set.
Our final system achieves 25% mixed error rate (MER) on the ASCEND
English/Mandarin code-switching test set -- reducing the MER by 2.1% absolute
compared to the previous literature -- while maintaining good accuracy on the
monolingual test sets.

本研究探讨了如何针对混杂语音优化基于神经传递器的双语自动语音识别系统，发现半监督训练和合成的混合开关数据可以改善这一系统的性能。同时，我们分析了每个神经传递器编码器对混合开关性能的影响，并在 ASCEND 数据集上评估了我们的英语 / 普通话系统，其混合错误率为 25％，较文献节省 2.1％，同时保持好的单语测试集准确性。