  Existing speech recognition systems are typically built at the sentence
level, although it is known that dialog context, e.g. higher-level knowledge
that spans across sentences or speakers, can help the processing of long
conversations. The recent progress in end-to-end speech recognition systems
promises to integrate all available information (e.g. acoustic, language
resources) into a single model, which is then jointly optimized. It seems
natural that such dialog context information should thus also be integrated
into the end-to-end models to improve further recognition accuracy. In this
work, we present a dialog-context aware speech recognition model, which
explicitly uses context information beyond sentence-level information, in an
end-to-end fashion. Our dialog-context model captures a history of
sentence-level context so that the whole system can be trained with
dialog-context information in an end-to-end manner. We evaluate our proposed
approach on the Switchboard conversational speech corpus and show that our
system outperforms a comparable sentence-level end-to-end speech recognition
system.
