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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HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Wei-Ning Hsu,Benjamin Bolte,Yao-Hung Hubert Tsai,Kushal Lakhotia,Ruslan Salakhutdinov,Abdelrahman Mohamed +5 more
TL;DR: HuBERT as mentioned in this paper utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss, which forces the model to learn a combined acoustic and language model over the continuous inputs.
Proceedings ArticleDOI
Paragraph text segmentation into lines with Recurrent Neural Networks
TL;DR: A new method to use more “agnostic” Machine Learning-based approaches to address text line location, inspired by the latest generation of optical models used for text recognition, namely Recurrent Neural Networks.
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Universal Phone Recognition with a Multilingual Allophone System.
Xinjian Li,Siddharth Dalmia,Juncheng Li,Matthew Lee,Patrick Littell,Jiali Yao,Antonios Anastasopoulos,David R. Mortensen,Graham Neubig,Alan W. Black,Florian Metze +10 more
TL;DR: This work proposes a joint model of both language-independent phone and language-dependent phoneme distributions that can build a (nearly-)universal phone recognizer that, when combined with the PHOIBLE [1] large, manually curated database of phone inventories, can be customized into 2,000 language dependent recognizers.
Proceedings ArticleDOI
Full-Page Text Recognition: Learning Where to Start and When to Stop
TL;DR: In this article, a new approach for full page text recognition is proposed based on regressions with Fully Convolutional Neural Networks and Multidimensional Long Short-Term Memory as contextual layers.
Proceedings ArticleDOI
Dense Temporal Convolution Network for Sign Language Translation.
TL;DR: A dense temporal convolution network, termed DenseTCN which captures the actions in hierarchical views and addresses the SLT problem by different views, including embedded short-term and extended longterm sequential learning.
References
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