A Teacher-Student Learning Approach for Unsupervised Domain Adaptation of Sequence-Trained ASR Models

Abstract

Teacher-student (T-S) learning is a transfer learning approach, where a teacher network is used to “teach” a student network to make the same predictions as the teacher. Originally formulated for model compression, this approach has also been used for domain adaptation, and is particularly effective when parallel data is available in source and target domains. The standard approach uses a frame-level objective of minimizing the KL divergence between the frame-level posteriors of the teacher and student networks. However, for sequence-trained models for speech recognition, it is more appropriate to train the student to mimic the sequence-level posterior of the teacher network. In this work, we compare this sequence-level KL divergence objective with another semi-supervised sequence-training method, namely the lattice-free MMI, for unsupervised domain adaptation. We investigate the approaches in multiple scenarios including adapting from clean to noisy speech, bandwidth mismatch and channel mismatch.