In recent years, end-to-end speech recognition has emerged as a technology that integrates the acoustic, pronunciation dictionary, and language model components of the traditional Automatic Speech Recognition model. It is possible to achieve human-like recognition without the need to build a pronunciation dictionary in advance. However, due to the relative scarcity of training data on code-switching, the performance of ASR models tends to degrade drastically when encountering this phenomenon. Most past studies have simplified the learning complexity of the model by splitting the code-switching task into multiple tasks dealing with a single language and then learning the domain-specific knowledge of each language separately. Therefore, in this paper, we attempt to introduce language identification information into the middle layer of the ASR model's encoder. We aim to generate acoustic features that imply language distinctions in a more implicit way, reducing the model's confusion when dealing with language switching.

翻译：近年来，端到端语音识别已成为一种融合传统自动语音识别模型中声学模型、发音词典和语言模型组件的技术。它无需预先构建发音词典即可实现类似人类的识别能力。然而，由于语码转换训练数据相对稀缺，语音识别模型在遇到这一现象时性能会大幅下降。过去大多数研究通过将语码转换任务分解为多个处理单一语言的任务，并分别学习每种语言的领域特定知识，从而简化模型的学习复杂度。因此，本文尝试将语言识别信息引入语音编码器中间层，以更隐式的方式生成蕴含语言区分的声学特征，降低模型在应对语言切换时的混淆程度。