  We investigate the feasibility of sequence-level knowledge distillation of
Sequence-to-Sequence (Seq2Seq) models for Large Vocabulary Continuous Speech
Recognition (LVSCR). We first use a pre-trained larger teacher model to
generate multiple hypotheses per utterance with beam search. With the same
input, we then train the student model using these hypotheses generated from
the teacher as pseudo labels in place of the original ground truth labels. We
evaluate our proposed method using Wall Street Journal (WSJ) corpus. It
achieved up to $ 9.8 \times$ parameter reduction with accuracy loss of up to
7.0\% word-error rate (WER) increase
