scholarly journals Improving Pronunciation for Non-Native Speakers Using Neural Networks

2021 ◽  
Vol 9 (VII) ◽  
pp. 3892-3895
Author(s):  
R Santhoshi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Speech Processing ◽  
Native Speakers ◽  
Native Language ◽  
Research Work ◽  
The Internet ◽  
Trial And Error ◽  
Audio Clip ◽  
Or Applications

While learning a new language through the internet or applications, a lot of them focus on teaching words and sentences and do not concentrate on the pronouncing ability of the users. Even though many speakers are proficient in a language, their pronunciation might be influenced by their native language. For people who are interested in improving their pronunciation capabilities this proposed system was introduced. This system is primarily focused on improving the pronunciation of English words and sentences for non-native speakers i.e., for whom English is a second language. For a given audio clip, we scale the audio and extract the features, input the features to the model developed and the output of the model gives the phonemes that are spoken in the clip. Many models detect phonemes and various methods have been proposed but the main reason for choosing deep learning is that the learning and the features that we tend to oversee or overlook are picked up by the model provided the dataset is balanced and the model is built properly. The features to be considered varies for every speech processing project and through previous research work and through trial and error we choose the features that work best for us. Comparing the phonemes with the actual phonemes present, we can give the speaker which part of their speech they need to work on. Based on the phoneme, feedback is given on how to improve their pronunciation.

scholarly journals Telugu and Hindi Script Recognition using Deep learning Techniques

2019 ◽  
Vol 8 (11) ◽  
pp. 1758-1764 ◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Research Work ◽  
Indian Languages ◽  
Digital Form ◽  
Training Time ◽  
Learning Techniques ◽  
Handwritten Document

The need for offline handwritten character recognition is intense, yet difficult as the writing varies from person to person and also depends on various other factors connected to the attitude and mood of the person. However, we are able to achieve it by converting the handwritten document into digital form. It has been advanced with introducing convolutional neural networks and is further productive with pre-trained models which have the capacity of decreasing the training time and increasing accuracy of character recognition. Research in recognition of handwritten characters for Indian languages is less when compared to other languages like English, Latin, Chinese etc., mainly because it is a multilingual country. Recognition of Telugu and Hindi characters are more difficult as the script of these languages is mostly cursive and are with more diacritics. So the research work in this line is to have inclination towards accuracy in their recognition. Some research has already been started and is successful up to eighty percent in offline hand written character recognition of Telugu and Hindi. The proposed work focuses on increasing accuracy in less time in recognition of these selected languages and is able to reach the expectant values.

Deep Learning With PyTorch

2020 ◽  
pp. 61-95
Author(s):  
Anmol Chaudhary ◽  
Kuldeep Singh Chouhan ◽  
Jyoti Gajrani ◽  
Bhavna Sharma
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Machine Translation ◽  
Exponential Growth ◽  
Graphics Processing Units ◽  
New Technologies ◽  
The Internet ◽  
Computational Power ◽  
Practical Applications ◽  
Graphics Processing

In the last decade, deep learning has seen exponential growth due to rise in computational power as a result of graphics processing units (GPUs) and a large amount of data due to the democratization of the internet and smartphones. This chapter aims to throw light on both the theoretical aspects of deep learning and its practical aspects using PyTorch. The chapter primarily discusses new technologies using deep learning and PyTorch in detail. The chapter discusses the advantages of using PyTorch compared to other deep learning libraries. The chapter discusses some of the practical applications like image classification and machine translation. The chapter also discusses the various frameworks built with the help of PyTorch. PyTorch consists of various models that increases its flexibility and accessibility to a greater extent. As a result, many frameworks built on top of PyTorch are discussed in this chapter. The authors believe that this chapter will help readers in getting a better understanding of deep learning making neural networks using PyTorch.

scholarly journals Advanced Stochastic Optimization Algorithm for Deep Learning Artificial Neural Networks in Banking and Finance Industries

2019 ◽  
Vol 1 (1) ◽  
pp. p8
Author(s):  
Jamilu Auwalu Adamu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Probability Distribution ◽  
Trial And Error ◽  
Activation Functions ◽  
Data Set ◽  
Artificial Neural

One of the objectives of this paper is to incorporate fat-tail effects into, for instance, Sigmoid in order to introduce Transparency and Stability into the existing stochastic Activation Functions. Secondly, according to the available literature reviewed, the existing set of Activation Functions were introduced into the Deep learning Artificial Neural Network through the “Window” not properly through the “Legitimate Door” since they are “Trial and Error “and “Arbitrary Assumptions”, thus, the Author proposed a “Scientific Facts”, “Definite Rules: Jameel’s Stochastic ANNAF Criterion”, and a “Lemma” to substitute not necessarily replace the existing set of stochastic Activation Functions, for instance, the Sigmoid among others. This research is expected to open the “Black-Box” of Deep Learning Artificial Neural networks. The author proposed a new set of advanced optimized fat-tailed Stochastic Activation Functions EMANATED from the AI-ML-Purified Stocks Data  namely; the Log – Logistic (3P) Probability Distribution (1st), Cauchy Probability Distribution (2nd), Pearson 5 (3P) Probability Distribution (3rd), Burr (4P) Probability Distribution (4th), Fatigue Life (3P) Probability Distribution (5th), Inv. Gaussian (3P) Probability Distribution (6th), Dagum (4P) Probability Distribution (7th), and Lognormal (3P) Probability Distribution (8th) for the successful conduct of both Forward and Backward Propagations of Deep Learning Artificial Neural Network. However, this paper did not check the Monotone Differentiability of the proposed distributions. Appendix A, B, and C presented and tested the performances of the stressed Sigmoid and the Optimized Activation Functions using Stocks Data (2014-1991) of Microsoft Corporation (MSFT), Exxon Mobil (XOM), Chevron Corporation (CVX), Honda Motor Corporation (HMC), General Electric (GE), and U.S. Fundamental Macroeconomic Parameters, the results were found fascinating. Thus, guarantee, the first three distributions are excellent Activation Functions to successfully conduct any Stock Deep Learning Artificial Neural Network. Distributions Number 4 to 8 are also good Advanced Optimized Activation Functions. Generally, this research revealed that the Advanced Optimized Activation Functions satisfied Jameel’s ANNAF Stochastic Criterion depends on the Referenced Purified AI Data Set, Time Change and Area of Application which is against the existing “Trial and Error “and “Arbitrary Assumptions” of Sigmoid, Tanh, Softmax, ReLu, and Leaky ReLu.

scholarly journals Multi-channel spectrograms for speech processing applications using deep learning methods

2020 ◽  
Author(s):  
T. Arias-Vergara ◽  
P. Klumpp ◽  
J. C. Vasquez-Correa ◽  
E. Nöth ◽  
J. R. Orozco-Arroyave ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Speech Processing ◽  
Single Channel ◽  
Audio Signal ◽  
Frequency Component ◽  
Feature Maps ◽  
Time Frequency ◽  
Convolutional Networks ◽  
Channel Input

Abstract Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature maps. In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. However, this may limit the potential of convolutional networks to learn different representations of the audio signal. In this paper, we propose a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to extract phone-attribute features. For this, two different deep learning-based models are considered: convolutional neural networks and recurrent neural networks with convolution layers.

scholarly journals Applications of Deep Learning on Topographic Images to Improve the Diagnosis for Dynamic Systems and Unconstrained Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Gharbi Alshammari ◽  
Abdulsattar Abdullah Hamad ◽  
Zeyad M. Abdullah ◽  
Abdulrhman M. Alshareef ◽  
Nawaf Alhebaishi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Dynamic Systems ◽  
Optimal Algorithm ◽  
Learning Algorithms ◽  
Research Work ◽  
Neural Network Learning ◽  
Network Learning ◽  
High Level

Studies carried out by researchers show that data growth can be exploited in such a way that the use of deep learning algorithms allow predictions with a high level of precision based on the data, which is why the latest studies are focused on the use of convolutional neural networks as the optimal algorithm for image classification. The present research work has focused on making the diagnosis of a disease that affects the cornea called keratoconus through the use of deep learning algorithms to detect patterns that will later be used to carry out preventive detections. The algorithm used to perform the classifications has been convolutional neural networks as well as image preprocessing to remove noise that can limit neural network learning, resulting in more than 1900 classified images out of a total of >2000 images distributed between normal eyes and those with keratoconus, which is equivalent to 92%.

scholarly journals Superintelligent Deep Learning Artificial Neural Networks

2019 ◽  
Author(s):  
Jamilu Adamu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Human Brain ◽  
Point Of View ◽  
Basic Unit ◽  
Trial And Error ◽  
Activation Functions ◽  
Data Set ◽  
Artificial Neural

Activation Functions are crucial parts of the Deep Learning Artificial Neural Networks. From the Biological point of view, a neuron is just a node with many inputs and one output. A neural network consists of many interconnected neurons. It is a “simple” device that receives data at the input and provides a response. The function of neurons is to process and transmit information; the neuron is the basic unit in the nervous system. Carly Vandergriendt (2018) stated the human brain at birth consists of an estimated 100 billion Neurons. The ability of a machine to mimic human intelligence is called Machine Learning. Deep Learning Artificial Neural Networks was designed to work like a human brain with the aid of arbitrary choice of Non-linear Activation Functions. Currently, there is no rule of thumb on the choice of Activation Functions, “Try out different things and see what combinations lead to the best performance”, however, sincerely; the choice of Activation Functions should not be Trial and error. Jamilu (2019) proposed that Activation Functions shall be emanated from AI-ML-Purified Data Set and its choice shall satisfy Jameel’s ANNAF Stochastic and or Deterministic Criterion. The objectives of this paper are to propose instances where Deep Learning Artificial Neural Networks are SUPERINTELLIGENT. Using Jameel’s ANNAF Stochastic and or Deterministic Criterion, the paper proposed four classes where Deep Learning Artificial Neural Networks are Superintelligent namely; Stochastic Superintelligent, Deterministic Superintelligent, and Stochastic-Deterministic 1st and 2nd Levels Superintelligence. Also, a Normal Probabilistic-Deterministic case was proposed.

Deep learning in Arabic sentiment analysis: An overview

2019 ◽  
pp. 016555151986548
Author(s):  
Amal Alharbi ◽  
Mounira Taileb ◽  
Manal Kalkatawi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
The Internet ◽  
Learning Approach ◽  
Comprehensive Overview ◽  
Exponential Increase ◽  
Arabic Sentiment Analysis

Sentiment analysis became a very motivating area in both academic and industrial fields due to the exponential increase of the online published reviews and recommendations. To solve the problem of analysing and classifying those reviews and recommendations, several techniques have been proposed. Lately, deep neural networks showed promising outcomes in sentiment analysis. The growing number of Arab users on the Internet along with the increasing amount of published Arabic reviews and comments encouraged researchers to apply deep learning to analyse them. This article is a comprehensive overview of research works that utilised the deep learning approach for Arabic sentiment analysis.

scholarly journals Identification of Covid’19 Vaccinator by Deep Learning Approach Using Contactless Palmprints

2021 ◽  
Vol 3 (3) ◽  
pp. 178-193
Author(s):  
B. Vivekanandam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Research Study ◽  
Research Work ◽  
Digital Camera ◽  
Activation Function ◽  
User Participation ◽  
Learning Platform

The invention of the first vaccine has also raised several anti-vaccination views among people. Vaccine reluctance may be exacerbated by the growing reliance on social media, which is considered as a source of health information. During this COVID'19 scenario, the verification of non-vaccinators via the use of biometric characteristics has received greater attention, especially in areas such as vaccination monitoring and other emergency medical services, among other things. The traditional digital camera utilizes the middle-resolution images for commercial applications in a regulated or contact-based environment with user participation, while the latter uses high-resolution latent palmprints. This research study attempts to utilize convolutional neural networks (CNN) for the first time to perform contactless recognition. To identify the COVID '19 vaccine using the CNN technique, this research work has used the contactless palmprint method. Further, this research study utilizes the PalmNet structure of convolutional neural network to resolve the issue. First, the ROI region of the palmprint was extracted from the input picture based on the geometric form of the print. After image registration, the ROI region is sent into a convolutional neural network as an input. The softmax activation function is then used to train the network so that it can choose the optimal learning rate and super parameters for the given learning scenario. The neural networks of the deep learning platform were then compared and summarized.

scholarly journals Complexity Neural Networks for Estimating Flood Process in Internet-of-Things Empowered Smart City

2020 ◽  
Vol 10 (6) ◽  
pp. 118-129
Author(s):  
Mustafa Ahmed Othman Abo Mhara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Internet Of Things ◽  
Water Conservation ◽  
Forecasting Model ◽  
The Internet ◽  
Hydrological Data ◽  
Complex Features ◽  
The Internet Of Things

With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecasting model based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyper parameters of the proposed CNN flood forecasting model.

scholarly journals Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms

2020 ◽  
Vol 9 (2) ◽  
pp. 79-90
Author(s):  
Noor Fatima
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Deep Neural Network ◽  
Optimization Algorithms ◽  
Trial And Error ◽  
Data Scientist ◽  
Insightful Analysis ◽  
And Performance

Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.

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