Explainable Connectionist-Temporal-Classification-Based Scene Text Recognition [PDF]
Journal of Imaging, 2023 Connectionist temporal classification (CTC) is a favored decoder in scene text recognition (STR) for its simplicity and efficiency. However, most CTC-based methods utilize one-dimensional (1D) vector sequences, usually derived from a recurrent neural ...
Rina Buoy +3 more
doaj +6 more sources
Information, 2021 Out-of-vocabulary (OOV) words are the most challenging problem in automatic speech recognition (ASR), especially for morphologically rich languages.
Eshete Derb Emiru +4 more
doaj +3 more sources
Integrating international Chinese visualization teaching and vocational skills training: leveraging attention-connectionist temporal classification models [PDF]
PeerJ Computer ScienceThe teaching of Chinese as a second language has become increasingly crucial for promoting cross-cultural exchange and mutual learning worldwide. However, traditional approaches to international Chinese language teaching have limitations that hinder ...
Yuan Yao, Zhujun Dai, Muhammad Shahbaz
doaj +3 more sources
Advancing Connectionist Temporal Classification with Attention Modeling [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Accepted at ICASSP ...
Amit Das, Jinyu Li
exaly +3 more sources
A CTC-Based Speech Recognition Network Fusing Local Convolution and Global Attention [PDF]
SensorsIntegrating wav2vec 2.0 with Connectionist Temporal Classification (CTC) for automatic speech recognition (ASR) often involves a trade-off between capturing global semantic consistency and maintaining local feature discriminability.
Huijuan Hu +3 more
doaj +2 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 ...
Liang Lu
exaly +3 more sources
Modeling Intra-label Dynamics and Analyzing the Role of Blank in Connectionist Temporal Classification [PDF]
Computer and Knowledge Engineering, 2018 The goal of many tasks in the realm of sequence processing is to map a sequence of input data to a sequence of output labels. Long short-term memory (LSTM), a type of recurrent neural network (RNN), equipped with connectionist temporal classification ...
Ashkan Sadeghi Lotfabadi +2 more
doaj +2 more sources
Training LDCRF model on unsegmented sequences using connectionist temporal classification [PDF]
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 2016 Many machine learning problems such as speech recognition, gesture recognition, and handwriting recognition are concerned with simultaneous segmentation and labeling of sequence data. Latent-dynamic conditional random field (LDCRF) is a well-known discriminative method that has been successfully used for this task.
Amir Ahooye Atashin +2 more
exaly +3 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.
Miloš Cernak
exaly +2 more sources
MPSA-Conformer-CTC/Attention: A High-Accuracy, Low-Complexity End-to-End Approach for Tibetan Speech Recognition [PDF]
SensorsThis study addresses the challenges of low accuracy and high computational demands in Tibetan speech recognition by investigating the application of end-to-end networks. We propose a decoding strategy that integrates Connectionist Temporal Classification
Changlin Wu +3 more
doaj +2 more sources