Machine Learning for Speech Recognition: A Literature Review and Its Application to Speech Recognition


Machine Learning for Speech Recognition: A Literature Review and Its Application to Speech Recognition – The purpose of this work is to propose a framework for automatic speech recognition based on convolutional neural networks (CNNs). In this work, we propose a novel and effective convolutional feedforward network architecture for speech recognition. By using the neural network as a convolutional neural network, training CNNs is fast and efficient; the cost of training is linear. This paper demonstrates the effectiveness of CNNs for speech recognition as well as for related tasks. To illustrate this improvement, we implement a new feature set for the classification of MNIST data, and use different feature sets for the input speech. Based on this network, we also propose the development of a new CNN for the classification of handwritten digits of Bengali speech as well as another CNN on MNIST data for speech recognition. The proposed framework is fully automatic and can be used for both speech recognition and human-robot interaction.

We provide a framework for identifying and ranking a set of items with a probabilistic model in the form of a hierarchy ranking graph. The problem of ranking items is often approached in this manner, in which the model is considered as a hierarchical network. This problem has been considered in many applications such as classification, classification of biological data, and in particular clustering. We consider a simple but effective approach to this problem which allows us to identify informative items in multiple dimensions and ranks them by a hierarchical ranking graph. Using the proposed algorithm, we show that this task can be performed asynchronously and in the same manner as clustering. This is achieved by combining two methods: one based on temporal ordering of clusters and the other based on linear time delay of rank updates. We show that clustering over large hierarchical networks is computationally efficient with a high probability.

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Machine Learning for Speech Recognition: A Literature Review and Its Application to Speech Recognition

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  • An Adaptive Aggregated Convex Approximation for Log-Linear Models

    Learning to rank using hierarchical clusteringWe provide a framework for identifying and ranking a set of items with a probabilistic model in the form of a hierarchy ranking graph. The problem of ranking items is often approached in this manner, in which the model is considered as a hierarchical network. This problem has been considered in many applications such as classification, classification of biological data, and in particular clustering. We consider a simple but effective approach to this problem which allows us to identify informative items in multiple dimensions and ranks them by a hierarchical ranking graph. Using the proposed algorithm, we show that this task can be performed asynchronously and in the same manner as clustering. This is achieved by combining two methods: one based on temporal ordering of clusters and the other based on linear time delay of rank updates. We show that clustering over large hierarchical networks is computationally efficient with a high probability.


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