Simultaneous Neural Machine Translation using Connectionist Temporal Classification
Simultaneous machine translation is a variant of machine translation that starts the translation process before the end of an input. This task faces a trade-off between translation accuracy and latency. We have to determine when we start the translation for observed inputs so far, to achieve good practical performance. In this work, we propose a neural
Katsuki Chousa +2 more
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Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network
Nanopore sequencing is promising because of its long read length and high speed. During sequencing, a strand of DNA/RNA passes through a biological nanopore, which causes the current in the pore to fluctuate. During basecalling, context-dependent current
Jingwen Zeng +5 more
doaj +1 more source
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification [PDF]
EMNLP ...
Libovický, Jindřich, Helcl, Jindřich
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Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification [PDF]
Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC include adding a label "blank" during training. With this mechanism, CTC eliminates the need of segment alignment, and hence has been applied to various sequence-to-sequence ...
Bo-Ru Lu +4 more
openaire +2 more sources
Soft Active Electromyography Interface for Machine Learning‐Enabled Silent Speech Recognition
A soft, hand‐worn electromyography interface enables intent‐driven silent speech recognition without continuous facial attachment. The device integrates liquid‐metal interconnects, a transparent flexible circuit, and elastomer encapsulation with a fingertip electrode that contacts perioral muscles only on demand.
Yuta Kurotaki +8 more
wiley +1 more source
Bidirectional Representations for Low-Resource Spoken Language Understanding
Speech representation models lack the ability to efficiently store semantic information and require fine tuning to deliver decent performance. In this research, we introduce a transformer encoder–decoder framework with a multiobjective training strategy,
Quentin Meeus +2 more
doaj +1 more source
Graph Connectionist Temporal Classification for Phoneme Recognition
Accepted to the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2025)
Grafé, Henry, Van hamme, hugo
openaire +3 more sources
Digital display instrument identification is a crucial approach for automating the collection of digital display data. In this study, we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital ...
Xuanzhang Wen +5 more
doaj +1 more source
Information Dynamics and Learning in Complex Adaptive Systems: Toward a Transdisciplinary Framework
ABSTRACT This article develops a framework for understanding learning and adaptation in complex adaptive systems. Drawing from neuroscience, systems theory, information theory and quantum field theory, it examines how information processing, plasticity and systemic coherence emerge from distributed, nonlinear and feedback‐driven interactions. It argues
Anderson de Souza Sant'Anna
wiley +1 more source
Attention-based CNN-ConvLSTM for Handwritten Arabic Word Extraction
Word extraction is one of the most critical steps in handwritten recognition systems. It is challenging for many reasons, such as the variability of handwritten writing styles, touching and overlapping characters, skewness problems, diacritics ...
takwa Ben Aicha, Afef Kacem Echi
doaj +1 more source

