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arXiv · 1609.03528

The Microsoft 2016 Conversational Speech Recognition System

W. Xiong·J. Droppo·X. Huang·F. Seide·M. Seltzer·A. Stolcke·D. Yu·G. Zweig
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Abstract

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.

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W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig. 2016-09-12. The Microsoft 2016 Conversational Speech Recognition System. https://doi.org/10.1109/icassp.2017.7953159

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