Improving Speech Recognition with Neural Networks


Improving Speech Recognition with Neural Networks – In this work, we propose ToSAR, a deep reinforcement learning (RL) robot that uses its speech recognition capabilities for natural language processing. ToSAR is an automatic saliency-based recurrent agent that learns to distinguish text from images, therefore solving the problem of speech recognition from natural context. ToSAR is trained on real-world data, which involves a speech recognition problem and a human-robot interaction domain. The first approach is a two-stage learning approach that consists of using three different types of reinforcement learning (SRL), namely, learning from input and reinforcement learning, or neural-sensor-sensing, respectively. We design two variants of ToSAR learning module, namely, NeuralNet with a 3D neural network-based approach, and ToSAR that requires a human to be able to recognize input text given a natural context. ToSAR uses reinforcement learning techniques to learn from input and to predict future actions. ToSAR is evaluated on real-world and synthetic data and shows promising results.

We propose and analyze a framework for automatic segmentation of high-resolution face images by exploiting the temporal and spatial information. Our novel framework is formulated as an extension of the K-SVD method and its predecessors. It consists of a Convolutional Neural Network (CNN), a Convolutional Linear Network (CNNLN), a Convolutional Neural Network (CNN-DNN), Deep Convolutional Neural Network (CNN-DNN), and a Convolutional Neural Network (CNN-RNN). We demonstrate its ability to extract high-resolution face images and segment large-scale images while minimizing the task cost with a small training set size. The CNN is trained end-to-end. Our experimental results show that our approach outperforms the state-of-the-art approaches in terms of segmentation cost while obtaining lower annotations.

Learning Localized Metrics Through Stochastic Constraints Using Deep Convolutional Neural Networks

A General Framework of Multiview, Multi-Task Learning, and Continuous Stochastic Variational Inference

Improving Speech Recognition with Neural Networks

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  • Learning from Negative News by Substituting Negative Images with Word2vec

    Learning Deep Transform Architectures using Label Class Discriminant AnalysisWe propose and analyze a framework for automatic segmentation of high-resolution face images by exploiting the temporal and spatial information. Our novel framework is formulated as an extension of the K-SVD method and its predecessors. It consists of a Convolutional Neural Network (CNN), a Convolutional Linear Network (CNNLN), a Convolutional Neural Network (CNN-DNN), Deep Convolutional Neural Network (CNN-DNN), and a Convolutional Neural Network (CNN-RNN). We demonstrate its ability to extract high-resolution face images and segment large-scale images while minimizing the task cost with a small training set size. The CNN is trained end-to-end. Our experimental results show that our approach outperforms the state-of-the-art approaches in terms of segmentation cost while obtaining lower annotations.


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