Conversational automatic speech recognition (ASR) is a task to recognize
conversational speech including multiple speakers. Unlike sentence-level ASR,
conversational ASR can naturally take advantages from specific characteristics
of conversation, such as role preference and topical coherence. This paper
proposes a conversational ASR model which explicitly learns conversation-level
characteristics under the prevalent end-to-end neural framework. The highlights
of the proposed model are twofold. First, a latent variational module (LVM) is
attached to a conformer-based encoder-decoder ASR backbone to learn role
preference and topical coherence. Second, a topic model is specifically adopted
to bias the outputs of the decoder to words in the predicted topics.
Experiments on two Mandarin conversational ASR tasks show that the proposed
model achieves a maximum 12% relative character error rate (CER) reduction.

本文提出一种基于端到端神经网络框架的会话自动语音识别模型，该模型使用潜变量模块来学习会话级别的角色偏好和主题连贯性，并使用一个主题模型来偏置解码器的输出，以预测主题中的单词。在两个汉语会话自动语音识别任务上的实验表明，该模型实现了最大 12% 相对字符错误率的降低。