Investigation of Sequence-level Knowledge Distillation Methods for CTC Acoustic Models

Abstract

This paper presents knowledge distillation (KD) methods for training connectionist temporal classification (CTC) acoustic models. In a previous study, we proposed a KD method based on the sequence-level cross-entropy, and showed that the conventional KD method based on the frame-level cross-entropy did not work effectively for CTC acoustic models, whereas the proposed method improved the performance of the models. In this paper, we investigate the implementation of sequence-level KD for CTC models and propose a lattice-based sequence-level KD method. Experiments investigating model compression and the training of a noise-robust model using the Wall Street Journal (WSJ) and CHiME4 datasets demonstrate that the sequence-level KD methods improve the performance of CTC acoustic models on both two tasks, and show that the lattice-based method can compute the sequence-level KD more efficiently than the N-best-based method proposed in our previous work.