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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Journal ArticleDOI
Decoupled Attention Network for Text Recognition
Tianwei Wang,Yuanzhi Zhu,Lianwen Jin,Canjie Luo,Xiaoxue Chen,Yaqiang Wu,Qianying Wang,Mingxiang Cai +7 more
TL;DR: Wang et al. as mentioned in this paper proposed a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results, and achieved state-of-the-art performance on multiple text recognition tasks.
Proceedings ArticleDOI
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
TL;DR: In this article, Fang et al. proposed an autonomous, bidirectional and iterative ABINet for scene text recognition, which blocks gradient flow between vision and language models to enforce explicitly language modeling.
Journal ArticleDOI
Offline continuous handwriting recognition using sequence to sequence neural networks
TL;DR: A new neural network architecture that combines a deep convolutional neural network with an encoder–decoder, called sequence to sequence, to solve the problem of recognizing isolated handwritten words to recognize any given word is proposed.
Proceedings ArticleDOI
Direct Acoustics-to-Word Models for English Conversational Speech Recognition
TL;DR: This paper presents the first results employing direct acoustics-to-word CTC models on two well-known public benchmark tasks: Switchboard and CallHome, and presents rescoring results on CTC word model lattices to quantify the performance benefits of a LM, and contrast the performance of word and phone C TC models.
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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.