Speaker-independent expressive voice synthesis using learning-based hybrid network model

Vekkot, Susmitha; Gupta, Deepa

doi:10.1007/s10772-020-09691-1

Cited by 12 publications

(6 citation statements)

References 52 publications

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“…From a statistical point of view, machine learning technology is to predict the distribution of data, learn a model from data, and then use this model to predict new data [17]. Neural network is the main algorithm and means of DL, which is divided into CNN [18] and deep belief nets (DBN) [19].…”

Section: ) DLmentioning

confidence: 99%

The Application of Information Classification in Agricultural Production Based on Internet of Things and Deep Learning

Gao

2022

IEEE Access

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Under the China's increasing attention to the technological innovation of agricultural production, all kinds of agricultural information have exploded on the internet, and agricultural informatization has developed rapidly. The related information has spread all over the whole network, which makes it gradually difficult to extract useful information from the network. To solve the deficiency of information classification ability of traditional agricultural information collection methods, the classification method of agricultural information is optimized, to realize searching the required agricultural information quickly. At first, the study introduces Deep Learning (DL) technology and the Internet of Things (IoT) and their advantages. Then, based on Bayesian Networks (BN) and Decision Tree (DT) algorithm, the agricultural information classification model is implemented and trained. Using various agricultural economic development theories, analyzation is made on the present situation of domestic agricultural informatization development. Finally, the advantages are put forward of agricultural production informatization development and economic management development based on IoT technology. The research results show that, the agricultural information classification model based on DL and IoT technology can accurately select the required effective information from the network, and the application of IoT technology in agricultural production big data plays an important role in production and economic management. Therefore, the agricultural information classification model based on DL and IoT technology can make an effective and accurate judgment on the classification of agricultural information, and then provide a focus for agricultural production and economic development. A new idea is provided for the application of new technologies in agricultural production and management.

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Section: ) DLmentioning

confidence: 99%

The Application of Information Classification in Agricultural Production Based on Internet of Things and Deep Learning

Gao

2022

IEEE Access

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“…As a mathematical function, artificial neurons imitate biological neurons' basic operational mechanism; thus, they have VOLUME 10, 2022 specific characteristics of biological neurons [27]. Its structure is shown in Fig.…”

Section: ) Artificial Neuronmentioning

confidence: 99%

“…Generally, the lower the proportion of negative samples identified positively in all negative samples is, the better the model performance is, as calculated by Eq. (27).…”

Section: G Partial Discharge Data Acquisitionmentioning

confidence: 99%

Exploration on Automatic Management of GIS Using TL-CNN and IoT

et al. 2022

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The research aims to effectively monitor the state of complex industrial equipment in real time, diagnose the internal Partial Discharge (PD) pattern of Gas Insulated Metal Enclosed Switchgear (GIS), implement effective health management, and change the traditional model optimization route of increasing training time in exchange for performance improvement. This paper studies the complex equipment-oriented Health Management System (HMS) based on Internet of Things (IoT) technology and Transfer Learning. Firstly, the principles of Transfer Learning and Deep Learning (DL) technology are introduced. Secondly, the requirements of GIS internal status recognition and management are studied. Furthermore, a GIS-oriented HMS based on Transfer Learning-optimized Convolutional Neural Networks (CNN) is proposed, and the training dataset is constructed. Finally, the proposed model is tested. The results show that the complex equipment-oriented HMS based on IoT technology, CNN, and Transfer Learning can detect the internal status of GIS in real time. Compared with the traditional DL algorithm and expert system, the proposed model has a shorter training time of only 16min, faster convergence speed, high testing recognition rate, and over 96% recognition rate. Compared with other mainstream algorithms, it has higher identification, the storage parameter volume is 408, and the storage space is 12.8MB. Moreover, the proposed Transfer Learning-optimized CNN model can accurately detect the status of GIS, identify abnormal statuses, and help prolong the service life of GIS. The proposed complex equipment-oriented HMS contributes to the intelligent manufacturing industry and provides a new direction for applying emerging Computer Technology in the intelligent industry.INDEX TERMS Internet of Things, convolutional neural network, GIS, status recognition, equipment health management. I. INTRODUCTIONChinese continuous industrialization has gradually entered the era of ''industry 4.0'', which helps to popularize industrial reform, thus affecting all walks of life through advanced technologies, such as Information Technology (IT). As a result, the manufacturing industry set off a new round of revolution towards intelligent manufacturing, and intelligent industrial equipment sees extensive application [1], [2]. In particular, Gas-Insulated Metal Closed Switchgear (GIS), [3] because of its unique advantages, such as small footprint, high reliability, and little electromagnetic contamination is widely used in modern power systems. However, due to the limited production, transmission, and installation conditions, GIS will haveThe associate editor coordinating the review of this manuscript and approving it for publication was Porfirio Tramontana .

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“…Its basic feature is that it tries to mimic the patterns in which neurons in the brain transmit and process information. Representatively, it is used in computer vision and natural language processing [ 7 ]. Obviously, deep learning is strongly related to neural network in machine learning, or we can call deep learning improved neural network algorithms.…”

Section: Construction Of the Scoring System For English Essaysmentioning

confidence: 99%

Machine Learning-Based Intelligent Scoring System for English Essays under the Background of Modern Information Technology

Hong-fu

2022

Computational Intelligence and Neuroscience

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This work is to reduce the workload of teachers in English teaching and improve the writing level of students, so as to provide a way for students to practice English composition scoring independently and satisfy the needs of college teachers and students for intelligent English composition scoring and intelligently generated comments. In this work, it firstly clarifies the teaching requirements of college English classrooms and expounds the principles and advantages of machine learning technology. Secondly, a three-layer neural network model (NNM) is constructed by using the multilayer perceptron (MLP), combined with the latent Dirichlet allocation (LDA) algorithm. Furthermore, three semantic representation vector technologies, including word vector, paragraph vector, and full-text vector feature, are used to represent the full-text vocabulary of English composition. Then, a model based on the K-nearest neighbors (kNN) algorithm is proposed to generate English composition evaluation, and a final score based on the extreme gradient boosting (XGBoost) model is proposed. Finally, a model dataset is constructed using 800 college students’ English essays for the CET-4 mock test, and the model is tested. The research results show that the semantic representation vector technology proposed can more effectively extract the lexical semantic features of English compositions. The XGBoost model and the kNN algorithm model are used to score and evaluate English compositions, which improves the accuracy of the scores. This makes the management of the entire scoring model more efficient and more accurate. It means that the model proposed is better than the traditional model in terms of evaluation accuracy. This work provides a new direction for the application of artificial intelligence technology in English teaching under the background of modern information technology.

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Speaker-independent expressive voice synthesis using learning-based hybrid network model

Cited by 12 publications

References 52 publications

The Application of Information Classification in Agricultural Production Based on Internet of Things and Deep Learning

The Application of Information Classification in Agricultural Production Based on Internet of Things and Deep Learning

Exploration on Automatic Management of GIS Using TL-CNN and IoT

Machine Learning-Based Intelligent Scoring System for English Essays under the Background of Modern Information Technology

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