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。