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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Character-Level Language Modeling with Hierarchical Recurrent Neural Networks
Kyuyeon Hwang,Wonyong Sung +1 more
TL;DR: This article proposed hierarchical RNN architectures, which consist of multiple modules with different timescales and operate with the character-level clock, which allows the existing RNN CLM training approaches to be directly applicable without any modifications.
Journal ArticleDOI
Understanding vision-based continuous sign language recognition
Neena Aloysius,M. Geetha +1 more
TL;DR: A detailed review of all the works in vision-based CSLR is presented, based on the methods they have followed, followed by a brief on sensor-based systems and benchmark databases.
Proceedings ArticleDOI
Handwriting Recognition of Historical Documents with Few Labeled Data
TL;DR: This work demonstrates how to train an HTR system with few labeled data and proposes a model-based normalization scheme which considers the variability in the writing scale at the recognition phase.
Journal ArticleDOI
Bidirectional deep architecture for Arabic speech recognition
TL;DR: A general framework for Arabic speech recognition that uses Long Short-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the nonuniform sequence length of the speech utterances issued from feature extraction techniques and the obtained results show the superiority of the proposed approach.
Proceedings ArticleDOI
Unconstrained scene text and video text recognition for Arabic script
TL;DR: The effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes is demonstrated and the ability of RNNs to model contextual dependencies yields superior recognition results.
References
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TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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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.