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
More filters
Posted Content
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
TL;DR: This paper provides a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets; and objective functions.
Journal ArticleDOI
Fast multi-language LSTM-based online handwriting recognition
Victor Carbune,Pedro Gonnet,Thomas Deselaers,Henry Allan Rowley,Alexander N. Daryin,Marcos Calvo,Li-Lun Wang,Daniel Keysers,Sandro Feuz,Philippe Gervais +9 more
TL;DR: An online handwriting system that is able to support 102 languages using a deep neural network architecture that completely replaced the previous segment-and-decode-based system and reduced the error rate by 20–40% relative for most languages is described.
Proceedings ArticleDOI
Word Spotting and Recognition Using Deep Embedding
TL;DR: This work proposes an End2End embedding framework which jointly learns both the text and image embeddings using state of the art deep convolutional architectures to enhance word spotting and recognition.
Posted Content
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
TL;DR: In this paper, a systematic literature review (SLR) is presented to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions, which serve the purpose of presenting state of the art results and techniques on OCR.
Journal ArticleDOI
End-to-End Online Writer Identification With Recurrent Neural Network
TL;DR: This paper proposes an end-to-end framework for online text-independent writer identification by using a recurrent neural network (RNN) to represent the handwriting data of a particular writer by set of random hybrid strokes.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
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.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
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.