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We propose providing additional utterance-level features as inputs to a deep neural network (DNN) to facilitate speaker, channel and background normalization. Modifications of the basic algorithm are developed which result in significant reductions in word error rates (WERs). The algorithms are shown to combine well with speaker adaptation by backpropagation, resulting in a 9% relative WER reduction...
We investigate the use of large state inventories and the softplus nonlinearity for on-device neural network based mobile speech recognition. Large state inventories are achieved by less aggressive context-dependent state tying, and made possible by using a bottleneck layer to contain the number of parameters. We investigate alternative approaches to the bottleneck layer, demonstrate the superiority...
While deep neural networks (DNNs) have become the dominant acoustic model (AM) for speech recognition systems, they are still dependent on Gaussian mixture models (GMMs) for alignments both for supervised training and for context dependent (CD) tree building. Here we explore bootstrapping DNN AM training without GMM AMs and show that CD trees can be built with DNN alignments which are better matched...
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