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
Recognizing handwritten Arabic words using grapheme segmentation and recurrent neural networks
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State-of-the-Art Speech Recognition Using Multi-Stream Self-Attention With Dilated 1D Convolutions
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Proceedings ArticleDOI
A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading
Ya Zhao,Rui Xu,Mingli Song +2 more
TL;DR: When trained on CMLR dataset, the proposed CSSMCM surpasses the performance of state-of-the-art lip reading frameworks, which confirms the effectiveness of explicit modeling of tones for Chinese Mandarin lip reading.
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Mask-Predict: Parallel Decoding of Conditional Masked Language Models.
TL;DR: The authors use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation, which allows for efficient iterative decoding.
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