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I'm trying to come up to speed on the use of deep neural networks for speech recognition and I'm confused about what exactly are the targets for the DNN. I understand that the DNN tries to predict a series on senones, ie the inputs to the network are a window of audio signal features (like MFCCs and their derivatives) and the output is a one-hot vector indicating the senone. But to train this, we must have a set of targets in the training data, ie targets that we get from somewhere else. From what I understand (I could be wrong of course), these targets generally are obtained from using some other existing system like an HMM-GMM based speech recognition system. But this doesn't make sense to me - that HMM-GMM system will have its own errors, so wouldn't using those senones as ground truth make the DNN based system only as good as the HMM-GMM system? How can a DNN improve on the HMM-GMM system in this case? Can someone please explain this?

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