A Study on the Dialogue System based on Deep Neural Network

2018 ◽  
Vol 20 (2) ◽  
pp. 293-310
Author(s):  
In-Young Jhee ◽  
◽  
Hee-Dong Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Dialogue System

scholarly journals Text Generation in Dialogue System via Deep Neural Network

2021 ◽  
Vol 2037 (1) ◽  
pp. 012047
Author(s):  
Xiaodong Shi ◽  
Yicheng Sun ◽  
Yang Ding ◽  
Dong Han ◽  
Jingyang Li
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Text Generation ◽  
Dialogue System

Dialogue System of Controlling Robot Based on the Theory of Finite-State Automata

2019 ◽  
Vol 20 (11) ◽  
pp. 686-695
Author(s):  
Yin Shuai ◽  
A. S. Yuschenko
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Deep Neural Network ◽  
Continuous Speech Recognition ◽  
Dialogue System ◽  
Dialogue Management ◽  
Special Mode ◽  
Finite State ◽  
General Mode

The article discusses the system of dialogue control manipulation robots. The analysis of the basic methods of automatic speech recognition, speech understanding, dialogue management, voice response synthesis in dialogue systems has been carried out. Three types of dialogue management are considered as "system initiative", "user initiative" and "combined initiative". A system of object-oriented dialog control of a robot based on the theory of finite state machines with using a deep neural network is proposed. The main difference of the proposed system lies in the separate implementation of the dialogue process and robot’s actions, which is close to the pace of natural dialogue control. This method of constructing a dialogue control robot allows system to automatically correct the result of speech recognition, robot’s actions based on tasks. The necessity of correcting the result of speech recognition and robot’s actions may be caused by the users’ accent, working environment noise or incorrect voice commands. The process of correcting speech recognition results and robot’s actions consists of three stages, respectively, in a special mode and a general mode. The special mode allows users to directly control the manipulator by voice commands. The general mode extends the capabilities of users, allowing them to get additional information in real time. At the first stage, continuous speech recognition is built by using a deep neural network, taking into account the accents and speech speeds of various users. Continuous speech recognition is a real-time voice to text conversion. At the second stage, the correction of the speech recognition result by managing the dialogue based on the theory of finite automata. At the third stage, the actions of the robot are corrected depending on the operating state of the robot and the dialogue management process. In order to realize a natural dialogue between users and robots, the problem is solved in creating a small database of possible dialogues and using various training data. In the experiments, the dialogue system is used to control the KUKA manipulator (KRC4 control) to put the desired block in the specified location, implemented in the Python environment using the RoboDK software. The processes and results of experiments confirming the operability of the interactive robot control system are given. A fairly high accuracy (92 %) and an automatic speech recognition rate close to the rate of natural speech were obtained.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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