Results 11 to 20 of about 76,886 (181)

Detection and Recognition of Bilingual Urdu and English Text in Natural Scene Images Using a Convolutional Neural Network–Recurrent Neural Network Combination with a Connectionist Temporal Classification Decoder [PDF]

Sensors
Urdu and English are widely used for visual text communications worldwide in public spaces such as signboards and navigation boards. Text in such natural scenes contains useful information for modern-era applications such as language translation for ...
Chan-Su Lee, Muhammad Umair, Muhammad Zubair   +2 more
exaly   +3 more sources

A study on constraining Connectionist Temporal Classification for temporal audio alignment

Interspeech 2022, 2022
Connectionist Temporal Classification (CTC) has become a standard for deep learning-based temporal alignment allowing relevant probabilistic distributions to be learned. However, by nature, CTC is a transcription objective that can be minimized without guaranteeing any alignment properties.
Teytaut, Yann, Bouvier, Baptiste, Roebel, Axel   +2 more
openaire   +3 more sources

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, Katrin Kirchhoff, Zhiheng Huang   +2 more
openaire   +4 more sources

Recognition of vehicle license plates in highway scenes with deep fusion network and connectionist temporal classification

IET Image Processing
License plate recognition is crucial in Intelligent Transportation Systems (ITS) for vehicle management, traffic monitoring, and security inspection.
Liru Hua, Xinyi Ma, Chihang Zhao, Bailing Zhang, Zijun Su, Yuhang Wu   +5 more
doaj   +2 more sources

Attention-Enhanced Connectionist Temporal Classification for Discrete Speech Emotion Recognition [PDF]

Interspeech 2019, 2019
Discrete speech emotion recognition (SER), the assignment of a single emotion label to an entire speech utterance, is typically performed as a sequence-to-label task. This approach, however, is limited, in that it can result in models that do not capture temporal changes in the speech signal, including those indicative of a particular emotion.
Ziping Zhao 0001, Zhongtian Bao, Zixing Zhang 0001, Nicholas Cummins, Haishuai Wang, Björn W. Schuller   +5 more
openaire   +3 more sources

End-to-End Sequence Labeling via Convolutional Recurrent Neural Network with a Connectionist Temporal Classification Layer

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, Lisheng Qiao, Wentao Yu, Jing Li, Yanzhou Ma   +4 more
doaj   +2 more sources

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, Stefan Fiel, Robert Sablatnig   +2 more
openaire   +2 more sources

Graph Connectionist Temporal Classification for Phoneme Recognition

2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Accepted to the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2025)
Grafé, Henry, Van hamme, hugo
openaire   +4 more sources

Nasal Speech Sounds Detection Using Connectionist Temporal Classification [PDF]

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Phone attributes, known also as distinctive or phonological features, belong to important classification of the speech sounds used in automatic speech processing. Training of conventional phone attribute detectors (classifiers), either based on acoustic measurements or deep learning approaches, requires decent phone boundary segmentation.
Cernak, Milos, Tong, Sibo
openaire   +3 more sources

A Study of All-Convolutional Encoders for Connectionist Temporal Classification [PDF]

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Accepted to ICASSP ...
Kalpesh Krishna, Liang Lu 0001, Kevin Gimpel, Karen Livescu   +3 more
openaire   +4 more sources