Proceedings ArticleDOI
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves,Santiago Fernández,Faustino Gomez,Jürgen Schmidhuber +3 more
- pp 369-376
TLDR
This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing.Abstract:
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.read more
Citations
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Proceedings ArticleDOI
A Scalable Handwritten Text Recognition System
TL;DR: This article proposed a line recognition model based on neural networks without recurrent connections, which achieved a comparable accuracy with LSTM-based models while allowing for better parallelism in training and inference.
Proceedings ArticleDOI
The A2iA Multi-lingual Text Recognition System at the Second Maurdor Evaluation
Bastien Moysset,Theodore Bluche,Maxime Knibbe,Mohamed Faouzi BenZeghiba,Ronaldo Messina,Jérôme Louradour,Christopher Kermorvant +6 more
TL;DR: A system based on recurrent neural networks and weighted finite state transducers was used both for printed and handwritten recognition, in French, English and Arabic, for multi-lingual text recognition.
Posted Content
Sequence-to-sequence neural network models for transliteration.
Mihaela Rosca,Thomas M. Breuel +1 more
TL;DR: It is demonstrated that neural sequence-to-sequence models obtain state of the art or close to state-of-the- art results on existing datasets.
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E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
TL;DR: The experiments show that obtaining accurate multi-language multi-script annotations is a challenging problem and the proposed end-to-end method achieves state-of-the-art performance for both joint localization and script identification in natural images and in cropped word script identification.
Proceedings ArticleDOI
State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention with Dilated 1D Convolutions
TL;DR: A new neural network model architecture, namely multi-stream self-attention, is proposed to address the issue thus make the self-Attention mechanism more effective for speech recognition and achieve the word error rate of 2.2% on the test-clean dataset of the LibriSpeech corpus.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.