Gated Embeddings in End-to-End Speech Recognition
for Conversational-Context Fusion
Abstract
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. Specifi-
cally, we propose to use text-based external
word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework,
yielding significant improvement in word error rate with better conversational-context representation. We evaluated the models on the
Switchboard conversational speech corpus and
show that our model outperforms standard
end-to-end speech recognition models