Results 151 to 160 of about 3,274,646 (190)

A Novel Tactile Learning Assistive Tool for the Visually and Hearing Impaired with 3D-CNN and Bidirectional LSTM Leveraging Morse Code Technology. [PDF]

Bioengineering (Basel)
Puthu Vedu SZ, Altulyan M, Singh PK.
europepmc   +1 more source

Linguistic-visual based multimodal Yi character recognition. [PDF]

Sci Rep
Sun H, Ding X, Li Z, Sun J, Yu H, Zhang J.   +5 more
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A dual-stream deep learning framework for continuous sign language recognition to enhance communication accessibility in the Ha'il region. [PDF]

Sci Rep
Harrouch H, Guesmi H, Alalfy H, Jebali M.   +3 more
europepmc   +1 more source

Dynamic Vision-Based Non-Contact Rotating Machine Fault Diagnosis with EViT. [PDF]

Sensors (Basel)
Jin Z, Sun C, Li X.
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Feasibility Study of Parkinson's Speech Disorder Evaluation With Pre-Trained Deep Learning Model for Speech-to-Text Analysis. [PDF]

Korean J Neurotrauma
Kim KH, Lee BJ, Koo HW.
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Variational Connectionist Temporal Classification

Lecture Notes in Computer Science, 2020
Connectionist Temporal Classification (CTC) is a training criterion designed for sequence labelling problems where the alignment between the inputs and the target labels is unknown. One of the key steps is to add a blank symbol to the target vocabulary. However, CTC tends to output spiky distributions since it prefers to output blank symbol most of the
Jingdong Chen
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Multi-label Connectionist Temporal Classification

2019 International Conference on Document Analysis and Recognition (ICDAR), 2019
The Connectionist Temporal Classification (CTC) loss function [1] enables end-to-end training of a neural network for sequence-to-sequence tasks without the need for prior alignments between the input and output. CTC is traditionally used for training sequential, single-label problems; each element in the sequence has only one class.
Scott Cohen, Curtis Wigington, Brian Price   +2 more
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Sampled Connectionist Temporal Classification

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
This article introduces and evaluates Sampled Connectionist Temporal Classification (CTC) which connects the CTC criterion to the Cross Entropy (CE) objective through sampling. Instead of computing the logarithm of the sum of the alignment path likelihoods, at each training step the sampled CTC only computes the CE loss between the sampled alignment ...
Kamel Lahouel, Erik McDermott, Ehsan Variani   +2 more
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Connectionist Temporal Classification

Studies in Computational Intelligence, 2012
This chapter introduces the connectionist temporal classification (CTC) output layer for recurrent neural networks (Graves et al., 2006). As its name suggests, CTC was specifically designed for temporal classification tasks; that is, for sequence labelling problems where the alignment between the inputs and the target labels is unknown.
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Diffusion-Based Connectionist Temporal Classification

2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)
Jen-Tzung Chien
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