Results 21 to 30 of about 938 (162)
Sensors, 2020 Advanced automatic pronunciation error detection (APED) algorithms are usually based on state-of-the-art automatic speech recognition (ASR) techniques. With the development of deep learning technology, end-to-end ASR technology has gradually matured and ...
Long Zhang +7 more
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IEEE Access, 2021 Offline handwritten Chinese text recognition is one of the most challenging tasks in that it involves various writing styles, complex character-touching, and large number of character categories.
Yintong Wang +3 more
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Recognition of English speech – using a deep learning algorithm
Journal of Intelligent Systems, 2023 The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural ...
Wang Shuyan
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Self-attention Networks for Connectionist Temporal Classification in Speech Recognition [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks.
Julian Salazar +2 more
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American Sign Language Alphabet Recognition Using Inertial Motion Capture System with Deep Learning
Inventions, 2022 Sign language is designed as a natural communication method for the deaf community to convey messages and connect with society. In American sign language, twenty-six special sign gestures from the alphabet are used for the fingerspelling of proper words.
Yutong Gu +5 more
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International Journal of Computational Intelligence Systems, 2020 Sequence labeling is a common machine-learning task which not only needs the most likely prediction of label for a local input but also seeks the most suitable annotation for the whole input sequence.
Xiaohui Huang +4 more
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Brain-Inspired Polymer Dendrite Networks for Morphology-Dependent Computing Hardware. [PDF]
Adv Sci (Weinh)Although process variability is often perceived as a drawback in electronics, this work harnesses the stochastic nature of electropolymerization as a powerful ally for computation. The resulting conductive polymer dendrites exhibit unique structure‐property relationships and support in memory computing, paving the way for the development of a new class
Scholaert C +3 more
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Applied Sciences, 2021 We solve the problem of how to densely align actions in videos at frame level, with only the order of occurring actions available, in order to save the time-consuming efforts to accurately annotate the temporal boundaries of each action. We propose three
Lin Wang +3 more
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Word Beam Search: A Connectionist Temporal Classification Decoding Algorithm [PDF]
2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018 Recurrent Neural Networks (RNNs) are used for sequence recognition tasks such as Handwritten Text Recognition (HTR) or speech recognition. If trained with the Connectionist Temporal Classification (CTC) loss function, the output of such a RNN is a matrix containing character probabilities for each time-step.
Harald Scheidl +2 more
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A Helium Speech Unscrambling Algorithm Based on Deep Learning
Information, 2023 Helium speech, the language spoken by divers in the deep sea who breathe a high-pressure helium–oxygen mixture, is almost unintelligible. To accurately unscramble helium speech, a neural network based on deep learning is proposed.
Yonghong Chen, Shibing Zhang
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