  We present a novel large-context end-to-end automatic speech recognition
(E2E-ASR) model and its effective training method based on knowledge
distillation. Common E2E-ASR models have mainly focused on utterance-level
processing in which each utterance is independently transcribed. On the other
hand, large-context E2E-ASR models, which take into account long-range
sequential contexts beyond utterance boundaries, well handle a sequence of
utterances such as discourses and conversations. However, the transformer
architecture, which has recently achieved state-of-the-art ASR performance
among utterance-level ASR systems, has not yet been introduced into the
large-context ASR systems. We can expect that the transformer architecture can
be leveraged for effectively capturing not only input speech contexts but also
long-range sequential contexts beyond utterance boundaries. Therefore, this
paper proposes a hierarchical transformer-based large-context E2E-ASR model
that combines the transformer architecture with hierarchical encoder-decoder
based large-context modeling. In addition, in order to enable the proposed
model to use long-range sequential contexts, we also propose a large-context
knowledge distillation that distills the knowledge from a pre-trained
large-context language model in the training phase. We evaluate the
effectiveness of the proposed model and proposed training method on Japanese
discourse ASR tasks.
