Semi-Supervised Deep Learning for Speech Recognition with Probabilistic Decision Trees – Convolutional Neural Networks (CNNs) have been successful to produce very good speech recognition results, but their performance is severely limited by the fact that they only learn the speech characteristics of the input. In this work we aim to learn a state-of-the-art feature representation of speech, and we show that it is sufficient to learn a non-linear non-linear feature representation for speech recognition. We show that this representation consists of a small number of hidden features which are represented as a sparse feature vector, and this representation is sufficient to learn a multi-layer model for speech recognition. We present and implement a framework for training or training a CNN, and demonstrate that it can be used for end-to-end speech recognition.

The objective of this paper is to propose an algorithm for computing a Bayesian stochastic model that is linear in the model parameters, rather than stochastic in their parameters. The proposed algorithm takes as input the model parameter values and performs a Bayesian search for the parameters at each time step. Since the Bayesian search involves an infinite loop, an algorithm based on the proposed algorithm could be used to automatically identify the optimal model. The paper discusses several Bayesian search problems from the literature.

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# Semi-Supervised Deep Learning for Speech Recognition with Probabilistic Decision Trees

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The Information Bottleneck Problem with Finite Mixture ModelsThe objective of this paper is to propose an algorithm for computing a Bayesian stochastic model that is linear in the model parameters, rather than stochastic in their parameters. The proposed algorithm takes as input the model parameter values and performs a Bayesian search for the parameters at each time step. Since the Bayesian search involves an infinite loop, an algorithm based on the proposed algorithm could be used to automatically identify the optimal model. The paper discusses several Bayesian search problems from the literature.