Prosodic Structure Prediction using Deep Self-attention Neural Network

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Hongjuan Ma

Keyword(s):

Neural Network ◽

Structure Prediction ◽

Speech Synthesis ◽

Great Influence ◽

Recognition System ◽

English Word ◽

Advanced Technology ◽

Average Error ◽

Prosodic Structure ◽

Training Error

With the increasing maturity of speech synthesis technology, on the one hand, it has been more and more widely used in people’s lives; on the other hand, it also brings more and more convenience to people. The requirements for speech synthesis systems are getting higher and higher. Therefore, advanced technology is used to improve and update the accent recognition system. This paper mainly introduces the word stress annotation technology combined with neural network speech synthesis technology. In Chinese speech synthesis, prosodic structure prediction has a great influence on naturalness. The purpose of this paper is to accurately predict the prosodic structure, which has become an important problem to be solved in speech synthesis. Experimental data show that the average error of samples in the network training process is lel/85, and the minimum value of the training error after 500 steps is 0.00013127, so the final sample average error is lel = 85 ∗ 0.0013127 = 0.112 < 0.5, and use the deep neural network (DNN) to train different parameters to obtain the conversion model, and then synthesize these conversion models, and finally achieve the effect of improving the synthesized sound quality.

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A Discrete Hopfield Neural Network Based MIS Finding Algorithm for Stems Selecting and Its Application in RNA Secondary Structure Prediction

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2008.00051 ◽

2009 ◽

Vol 31 (1) ◽

pp. 51-58

Author(s):

Qi LIU ◽

Yin ZHANG ◽

Xiu-Zi YE ◽

Rong-Dong YU

Keyword(s):

Neural Network ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Hopfield Neural Network ◽

Rna Secondary Structure Prediction

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OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

PLoS ONE ◽

10.1371/journal.pone.0245982 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0245982

Author(s):

Yawu Zhao ◽

Yihui Liu

Keyword(s):

Neural Network ◽

Secondary Structure ◽

Structure Prediction ◽

Short Term Memory ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Short Term ◽

Protein Secondary Structure Prediction ◽

Term Memory ◽

Long Short Term Memory

Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.

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Protein structure prediction based on BN-GRU method

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691320500459 ◽

2020 ◽

Vol 18 (06) ◽

pp. 2050045

Author(s):

Lina Yang ◽

Pu Wei ◽

Cheng Zhong ◽

Xichun Li ◽

Yuan Yan Tang

Keyword(s):

Neural Network ◽

Protein Structure ◽

Spatial Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Main Idea ◽

Traditional Methods ◽

Analysis Of Similarity ◽

New Feature ◽

Activity Mechanism

The spatial structure of the protein reflects the biological function and activity mechanism. Predicting the secondary structure of a protein is the basis content for predicting its spatial structure. Traditional methods based on statistics and sequential patterns do not achieve higher accuracy. In this paper, the application of BN-GRU neural network in protein structure prediction is discussed. The main idea is to construct a Gated Recurrent Unit (GRU) neural network. The GRU neural network can learn long-term dependencies. It can handle long sequences better than traditional methods. Based on this, BN is combined with GRU to construct a new network. Position Specific Scoring Matrix (PSSM) is used to associate with other features to build a completely new feature set. It can be proved that the application of BN on GRU can improve the accuracy of the results. The idea in this paper can also be applied to the analysis of similarity of other sequences.

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Enhancing protein backbone angle prediction by using simpler models of deep neural networks

Scientific Reports ◽

10.1038/s41598-020-76317-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Fereshteh Mataeimoghadam ◽

M. A. Hakim Newton ◽

Abdollah Dehzangi ◽

Abdul Karim ◽

B. Jayaram ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Structure Prediction ◽

Protein Structures ◽

Absolute Error ◽

Grand Challenge ◽

Protein Backbone ◽

The Neural Network ◽

Benchmark Datasets ◽

The Neural Networks

Abstract Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more complex neural networks then just could counterbalance the noise. From artificial intelligence and machine learning perspectives, problem representations and solution approaches do mutually interact and thus affect performance. We also argue that comparatively simpler predictors can more easily be reconstructed than the more complex ones. With these arguments in mind, we present a deep learning method named Simpler Angle Predictor (SAP) to train simpler DNN models that enhance protein backbone angle prediction. We then empirically show that SAP can significantly outperform existing state-of-the-art methods on well-known benchmark datasets: for some types of angles, the differences are 6–8 in terms of mean absolute error (MAE). The SAP program along with its data is available from the website https://gitlab.com/mahnewton/sap.

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