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Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize(More)
Deep neural networks have recently become the gold standard for acoustic modeling in speech recognition systems. The key computational unit of a deep network is a linear projection followed by a point-wise non-linearity, which is typically a logistic function. In this work, we show that we can improve generalization and make training of deep networks faster(More)
This paper presents a novel Bayesian approach to the problem of co-channel speech. The problem is formulated as the joint maximization of the a posteriori probability of the word sequence and the target speaker given the observed speech signal. It is shown that the joint probability can be expressed as the product of six terms: a likelihood score from a(More)
Necessary conditions for asymptotically optimal sliding-block or stationary codes for source coding and rate-constrained simulation of memoryless sources are presented and used to motivate a design technique for trellis-encoded source coding and rate-constrained simulation. The code structure has intuitive similarities to classic random coding arguments as(More)
We propose a new approach for designing compact acoustic models particularly suited to large systems that combine multiple model sets to represent distinct acoustic conditions or languages. We show that Gaussians based on mixtures of inverse covariances (MIC) with shared parameters can be clustered using an efficient Lloyd algorithm. As a result, more(More)