[PDF] Top 20 Identification of Spoken Language from Webcast Using Deep Convolutional Recurrent Neural Networks
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Identification of Spoken Language from Webcast Using Deep Convolutional Recurrent Neural Networks
... Five different types of feature vectors were extracted within all of the following experiments: 39 dimensional MFCCs (13-dimensional with delta and acceleration), 62 dimensional MFCC-SDC (13-dimensional MFCCs with ... See full document
5
Byte based Language Identification with Deep Convolutional Networks
... Language identification is an unsolved problem, certainly in the context of discriminating between very similar languages (Baldwin and Lui, ...emerged neural network architectures, coupled with ... See full document
7
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
... representation using a CRF, then decoded that representation into a ...a deep convolutional network directly as the state vector that is decoded into a ... See full document
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Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data
... ranking-based, deep learning approach to automatically identify stages in a sales process following the well-known AIDA (Awareness/Attention, Interest, Desire, and Ac- tion) model (Lewis, 1903; Dukesmith, 1904; ... See full document
7
Deep learning for smart agriculture: Concepts, tools, applications, and opportunities
... supports deep learning algorithms, including CNN, RNN, GAN and other variants, which can be used on Linux, Windows, and Mac ...of deep learning application programming interfaces (API) including basic ... See full document
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Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
... natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained ...learned from data rather ... See full document
10
Assessing the Corpus Size vs Similarity Trade off for Word Embeddings in Clinical NLP
... of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data-poor clinical NLP ...embeddings from ... See full document
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Image Captioning using Multimodal Embedding
... techniques from the domain of natural language ...image using the semantic features and the style of the text corpus are unable to combine the visual semantics of two different images being fed ...of ... See full document
6
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... representations from large-scale unlabelled ...as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...Various deep learning ... See full document
5
Joint Online Spoken Language Understanding and Language Modeling With Recurrent Neural Networks
... In spoken language understand- ing, with real time intent identification and seman- tic constituents extraction, the downstream sys- tems will be able to perform corresponding search or query while ... See full document
9
Unified Framework For Deep Learning Based Text Classification
... artificial neural networks, which are inspired by biological brain model made of ...typical deep learning architecture has three components namely input variables, hidden layers and output ...in ... See full document
5
Fake news identification on Twitter with hybrid CNN and RNN models
... of neural networks for the effective training of their ...messages from one user to another and how they react— specifically if they embraced or refrained from becoming evangelists and ... See full document
6
Human Fall Detection using Co Saliency Enhanced Deep Recurrent Convolutional Neural Networks
... This subsection describes the proposed deep RCN in detail. The proposed RCN in the 2nd block of Fig.1 can be further depicted by Fig.2. It shows the detailed layers and settings of the entire RCN architecture, ... See full document
8
Blind Navigation System using Artificial Intelligence
... Logits Layer, the final layer of our neural network is the logits layer, which will return the raw values for our predictions. The logit model is a regression model where the dependent variable (DV) is ... See full document
5
Arabic machine transliteration using an attention based encoder decoder model
... word from one alphabet to another while preserving the phonetic and orthographic aspects of the transliterated ...transliteration from Arabic to English, some Arabic letters such as “ ”, “ ” and “ ” do not ... See full document
12
Brain Tumor Classification Using Convolutional Neural Networks
... convolution neural network is used for automatic brain tumor ...taken from image ...train from the starting layer, we have to train the entire layer ... See full document
5
Resiliency in Deep Convolutional Neural Networks
... which gives a random number from zero to the size of the tensor of weights. Random function generator used here is a library in python which can be called by importing a random class which has randomint() method ... See full document
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A STUDY ON USING DEEP LEARNING TECHNOLOGIES IN CONVOLUTIONAL NEURAL NETWORKS FOR MULTIPLE OBJECTS IDENTIFICATION
... If you only need to identify a single object in image, you simply need to use a simple CNN network. But the problem is that when the image has many objects in the image, the problem becomes very complicated. We have to ... See full document
7
Deep convolutional neural networks capabilities for
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. We.[r] ... See full document
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Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. As a 21.[r] ... See full document
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