Previously reported results on this benchmark task. TheĬombined system has an error rate of 6.2%, representing an improvement over System uses a ResNet architecture acoustic model with RNNLM rescoring, andĪchieves a word error rate of 6.9% on the NIST 2000 Switchboard task. Here’s how to do it in Word, PowerPoint, and more. Word posterior-based system combination provide a 20% boost. There are a few different ways to dictate text in Microsoft Office depending on the software version you use. Language model rescoring with multiple forward and backward running RNNLMs, and MMI training provide significant gains for all acoustic model architectures. Inspired by machine learning ensemble techniques, the system uses a range ofĬonvolutional and recurrent neural networks. Modeling to advance the state of the art on the Switchboard recognition task. Zweig Download PDF Abstract: We describe Microsoft's conversational speech recognition system, in which weĬombine recent developments in neural-network-based acoustic and language
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