Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
Year: 2019

We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is consequently better at recognizing long conversations. Specifically, we propose to use text-based external word and/or sentence embeddings (i.e., fast-Text, BERT) within an end-to-end f