Journal ArticleDOI
A CNN-BiLSTM based hybrid model for Indian language identification
Himanish Shekhar Das,Pinki Roy +1 more
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TLDR
CNN based bidirectional long short-term memory (BiLSTM) model has been proposed for Indian language identification with special emphasis on Northeastern languages and results show that the ResNet-50 based model has achieved accuracy up to 98.10% as compare to 97.70% for VGG-16 based model.About:
This article is published in Applied Acoustics.The article was published on 2021-11-01. It has received 5 citations till now. The article focuses on the topics: Language identification & Spoken language.read more
Citations
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Journal ArticleDOI
Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools
Attila Biró,Katalin Tunde Janosi-Rancz,László Szilágyi,Antonio Cuesta-Vargas,Jaime Martin-Martin,Sándor M. Szilágyi +5 more
TL;DR: The main objective of this paper is to analyze and propose DEtection TRansformer (DETR) models, architectures, hyperparameters—recommendation, and specific procedures with simplified methods to achieve reasonable accuracy to support real-time textual object detection for further analysis.
Journal ArticleDOI
Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s
TL;DR: Zhang et al. as mentioned in this paper employed a Bi-LSTM (Bi-directional Long Short-Term Memory) structure, which not only draws on past information but also captures subsequent information, resulting in the prediction of image content subject to the context clues.
Journal ArticleDOI
Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s
TL;DR: Zhang et al. as mentioned in this paper employed a Bi-LSTM (Bi-directional Long Short-Term Memory) structure, which not only draws on past information but also captures subsequent information, resulting in the prediction of image content subject to the context clues.
Journal ArticleDOI
Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach
TL;DR: In this article , the authors compared and examined the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep CNN architectures trained on mammography images, and concluded that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.
Proceedings ArticleDOI
Review of Features and Classification for Spoken Indian Language Recognition using Deep Learning and Machine Learning Techniques
TL;DR: The most common and organic method of interpersonal communication is speech as discussed by the authors and researchers require a specific database, or previously recorded collection of information, for that specific recognition system when they seek to construct it.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Posted Content
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
TL;DR: DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.
Journal ArticleDOI
Spoken Language Recognition: From Fundamentals to Practice
Haizhou Li,Bin Ma,Kong Aik Lee +2 more
TL;DR: This paper attempts to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions of spoken language recognition, from both phonological and computational aspects.