  Knowledge distillation has been widely used to compress existing deep
learning models while preserving the performance on a wide range of
applications. In the specific context of Automatic Speech Recognition (ASR),
distillation from ensembles of acoustic models has recently shown promising
results in increasing recognition performance. In this paper, we propose an
extension of multi-teacher distillation methods to joint CTC-attention
end-to-end ASR systems. We also introduce three novel distillation strategies.
The core intuition behind them is to integrate the error rate metric to the
teacher selection rather than solely focusing on the observed losses. In this
way, we directly distill and optimize the student toward the relevant metric
for speech recognition. We evaluate these strategies under a selection of
training procedures on different datasets (TIMIT, Librispeech, Common Voice)
and various languages (English, French, Italian). In particular,
state-of-the-art error rates are reported on the Common Voice French, Italian
and TIMIT datasets.
