Student-teacher learning or knowledge distillation (KD) has been previously used to address data scarcity issue for training of speech recognition (ASR) systems. However, a limitation of KD training is that the student model classes must be a proper or improper subset of the teacher model classes. It prevents distillation from even acoustically similar languages if the character sets are not same. In this work, the aforementioned limitation is addressed by proposing a MUltilingual Student-Teacher (MUST) learning which exploits a posteriors mapping approach. A pre-trained mapping model is used to map posteriors from a teacher language to the student language ASR. These mapped posteriors are used as soft labels for KD learning. Various teacher ensemble schemes are experimented to train an ASR model for low-resource languages. A model trained with MUST learning reduces relative character error rate (CER) up to 9.5% in comparison with a baseline monolingual ASR.

学生-教师学习或知识蒸馏已被用于解决言语识别系统训练中数据稀缺问题，本文提出了一种多语言学生-教师学习方法，通过使用预训练的映射模型将教师语言的posterior映射到学生语言的声学模型，将这些映射过程中的posteriors作为知识蒸馏的软标签，通过不同的教师集成方案训练出适用于低资源语言的ASR模型，相比基准单语言ASR模型，使用多语言学生-教师学习训练的模型降低了相对字符错误率（CER）9.5%。

MUST: 低资源语音识别的多语种学生-教师学习方法