Transformer-based models have demonstrated their effectiveness in automatic
speech recognition (ASR) tasks and even shown superior performance over the
conventional hybrid framework. The main idea of Transformers is to capture the
long-range global context within an utterance by self-attention layers.
However, for scenarios like conversational speech, such utterance-level
modeling will neglect contextual dependencies that span across utterances. In
this paper, we propose to explicitly model the inter-sentential information in
a Transformer based end-to-end architecture for conversational speech
recognition. Specifically, for the encoder network, we capture the contexts of
previous speech and incorporate such historic information into current input by
a context-aware residual attention mechanism. For the decoder, the prediction
of current utterance is also conditioned on the historic linguistic information
through a conditional decoder framework. We show the effectiveness of our
proposed method on several open-source dialogue corpora and the proposed method
consistently improved the performance from the utterance-level
Transformer-based ASR models.

本文提出了一种基于 Transformer 的端到端架构方法，在自动语音识别中精确建模跨话语的语境依赖，通过引入上下文感知残余注意机制，对先前语音的上下文进行编码，同时，还采用条件解码器框架将历史语言信息融入到当前预测中，结果表明该方法在几种公开对话语料库上都取得了持续的改进

基于句间注意力机制改进基于 Transformer 的对话语音识别

Improving Transformer-based Conversational ASR by Inter-Sentential Attention Mechanism

We describe Microsoft's conversational speech recognition system, in which we
combine recent developments in neural-network-based acoustic and language
modeling to advance the state of the art on the Switchboard recognition task.
Inspired by machine learning ensemble techniques, the system uses a range of
convolutional and recurrent neural networks. I-vector modeling and lattice-free
MMI training provide significant gains for all acoustic model architectures.
Language model rescoring with multiple forward and backward running RNNLMs, and
word posterior-based system combination provide a 20% boost. The best single
system uses a ResNet architecture acoustic model with RNNLM rescoring, and
achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The
combined system has an error rate of 6.2%, representing an improvement over
previously reported results on this benchmark task.

本文介绍 Microsoft 的会话式语音识别系统，其中采用了近期发展的基于神经网络的声学和语言建模技术，结合机器学习集成技术，使用了多种卷积和循环神经网络，通过 i-vector 模型建模和不带晶格的 MMI 训练，在声学建模架构的所有方面都提供了显著的增益率，使用多个前向和后向运行的 RNNLM 进行语言模型重新加权以及基于词后概率的系统组合，提高了 20％. 最佳单一系统使用 ResNet 架构声学模型与 RNNLM 重新加权，可在 NIST 2000 Switchboard 任务上实现 6.9％的词错误率。 结合系统的误差率为 6.2％，这是对此基准测试中以前报告的结果的改进。