In this work, we seek to build effective code-switched (CS) automatic speech
recognition systems (ASR) under the zero-shot setting where no transcribed CS
speech data is available for training. Previously proposed frameworks which
conditionally factorize the bilingual task into its constituent monolingual
parts are a promising starting point for leveraging monolingual data
efficiently. However, these methods require the monolingual modules to perform
language segmentation. That is, each monolingual module has to simultaneously
detect CS points and transcribe speech segments of one language while ignoring
those of other languages -- not a trivial task. We propose to simplify each
monolingual module by allowing them to transcribe all speech segments
indiscriminately with a monolingual script (i.e. transliteration). This simple
modification passes the responsibility of CS point detection to subsequent
bilingual modules which determine the final output by considering multiple
monolingual transliterations along with external language model information. We
apply this transliteration-based approach in an end-to-end differentiable
neural network and demonstrate its efficacy for zero-shot CS ASR on
Mandarin-English SEAME test sets.

在零 - shot 场景下，我们提出了一种基于转写的方法，通过简化单语模块的操作并将语种转录成单一脚本，来促进有效的代码交换自动语音识别系统。该方法通过后续的双语模块来检测多语言转录点并综合外部语言模型信息，应用于端到端可微分的神经网络中，在普通话 - 英语 SEAME 测试集上证明了其有效性。