  How to effectively incorporate cross-utterance information cues into a neural
language model (LM) has emerged as one of the intriguing issues for automatic
speech recognition (ASR). Existing research efforts on improving
contextualization of an LM typically regard previous utterances as a sequence
of additional input and may fail to capture complex global structural
dependencies among these utterances. In view of this, we in this paper seek to
represent the historical context information of an utterance as
graph-structured data so as to distill cross-utterances, global word
interaction relationships. To this end, we apply a graph convolutional network
(GCN) on the resulting graph to obtain the corresponding GCN embeddings of
historical words. GCN has recently found its versatile applications on
social-network analysis, text summarization, and among others due mainly to its
ability of effectively capturing rich relational information among elements.
However, GCN remains largely underexplored in the context of ASR, especially
for dealing with conversational speech. In addition, we frame ASR N-best
reranking as a prediction problem, leveraging bidirectional encoder
representations from transformers (BERT) as the vehicle to not only seize the
local intrinsic word regularity patterns inherent in a candidate hypothesis but
also incorporate the cross-utterance, historical word interaction cues
distilled by GCN for promoting performance. Extensive experiments conducted on
the AMI benchmark dataset seem to confirm the pragmatic utility of our methods,
in relation to some current top-of-the-line methods.
