Session-level Language Modeling for Conversational Speech
Wayne Xiong, Lingfeng Wu, Jun Zhang, Andreas Stolcke · 2018
We propose to generalize language models for conversational speech recognition to allow them to operate across utterance boundaries and speaker changes, thereby capturing conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence.The model consists of a long-shortterm memory (LSTM) recurrent network that reads the entire word-level history of a conversation, as well as information about turn taking and speaker overlap, in order to predict each next word.The model is applied in a rescoring framework, where the word history prior to the current utterance is approximated with preliminary recognition results.In experiments in the conversational telephone speech domain (Switchboard) we find that such a model gives substantial perplexity reductions over a standard LSTM-LM with utterance scope, as well as improvements in word error rate.