Deep Learning-Based Speech Recognition: A Survey

Deep Learning-Based Speech Recognition: A Survey – Deep Learning-Based Speech Recognition (DL-SVR) is a technique for multi-task classification. However, DL-SVR uses the speech recognition ability of the human brain to learn a set of classifiers that is specific to the domain of the goal task. In this paper, we show that the classification performance of a deep neural network (DNN) with two convolutional neural networks is significantly improved over the state of the art. We present two state-of-the-art training methods for DL-SVR, together with an efficient algorithm to train a DNN. This approach is validated on a small corpus and a large set of datasets. Experiments have shown promising improvements over the state of the art in three tasks: speech recognition, image recognition, and word recognition.

This paper proposes a novel method for predicting the movement and location of a mouse from video of its environment, called Video Recurrent Neural Network (VRLN). VRLN has been widely used in motion recognition research and has been used with other robotic arm movement systems. The key idea is to combine the temporal dynamics and the spatial interactions between the two frames to predict the location of the mouse. We compare the behavior of a robot arm with the motion of the mouse in a video and evaluate it using a real-time video review system, where the robot arm is still interacting with the environment, with the mouse moving in a particular direction. At the same time, we propose a novel approach using an end-to-end network, which is more efficient and robust than the traditional methods in predicting the location of mouse. We evaluate the proposed method using a real-time video review system, which contains a computer, video and a camera.

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Deep Learning-Based Speech Recognition: A Survey

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  • Deep Learning for Improving Multi-Domain Image Retrieval

    Robot Learning Is Robust to Mouse MovementThis paper proposes a novel method for predicting the movement and location of a mouse from video of its environment, called Video Recurrent Neural Network (VRLN). VRLN has been widely used in motion recognition research and has been used with other robotic arm movement systems. The key idea is to combine the temporal dynamics and the spatial interactions between the two frames to predict the location of the mouse. We compare the behavior of a robot arm with the motion of the mouse in a video and evaluate it using a real-time video review system, where the robot arm is still interacting with the environment, with the mouse moving in a particular direction. At the same time, we propose a novel approach using an end-to-end network, which is more efficient and robust than the traditional methods in predicting the location of mouse. We evaluate the proposed method using a real-time video review system, which contains a computer, video and a camera.


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