Dialog-context aware end-to-end speech recognition
This work addresses the challenge of processing conversational speech for applications like voice assistants, though it is incremental as it builds on existing end-to-end methods.
The paper tackles the problem of improving speech recognition accuracy in long conversations by integrating dialog context into end-to-end models, showing that their system outperforms a sentence-level baseline on the Switchboard corpus.
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.