Learning Between Different Teacher and Student Models in ASR

Teacher-student learning can be applied in automatic speech recognition for model compression and domain adaptation. This trains a student model to emulate the behaviour of a teacher model, and only the student is used to perform recognition. Depending on the application, the teacher and student may differ in their model types, complexities, input contexts, and input features. In previous works, it is often shown that learning from a strong teacher allows the student to perform better than an equivalent model trained with only the reference transcriptions. However, there has not been much investigation into whether a particular form of teacher is appropriate for the student to learn from. This paper aims to study how effectively the student is able to learn from the teacher, when differences exist between their designs. The Augmented Multi-party Interaction (AMI) meeting transcription and Multi-Genre Broadcast (MGB-3) television broadcast audio tasks are used in this analysis. Experimental results suggest that a student can effectively learn from a more complex teacher, but may struggle when it lacks input information. It is therefore important to carefully consider the design of the student for each application.