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RESTRUCTURING DEEP NEURAL NETWORK ACOUSTIC MODELS

机译:重构深层神经网络声学模型

摘要

A Deep Neural Network (DNN) model used in an Automatic Speech Recognition (ASR) system is restructured. A restructured DNN model may include fewer parameters compared to the original DNN model. The restructured DNN model may include a monophone state output layer in addition to the senone output layer of the original DNN model. Singular value decomposition (SVD) can be applied to one or more weight matrices of the DNN model to reduce the size of the DNN Model. The output layer of the DNN model may be restructured to include monophone states in addition to the senones (tied triphone states) which are included in the original DNN model. When the monophone states are included in the restructured DNN model, the posteriors of monophone states are used to select a small part of senones to be evaluated.
机译:重构了自动语音识别(ASR)系统中使用的深度神经网络(DNN)模型。与原始DNN模型相比,重构的DNN模型可以包含更少的参数。重构的DNN模型除了原始DNN模型的senone输出层外,还可以包括单音器状态输出层。可以将奇异值分解(SVD)应用于DNN模型的一个或多个权重矩阵,以减小DNN模型的大小。除了原始DNN模型中包含的senone(并列三音器状态)外,DNN模型的输出层还可以重组为包括单音器状态。当单声道状态包含在重构的DNN模型中时,单声道状态的后验者用于选择要评估的一小部分Senone。

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