Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is consequently better at recognizing long conversations.
we propose to use text-based external word and/or sentence embeddings (i.e., fast-Text, BERT) within an end-to-end framework, yielding significant improvement in word error rate with better conversational-context representation.