scispace - formally typeset
Proceedings ArticleDOI

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

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
Journal ArticleDOI

A lexicon-free approach for 3D handwriting recognition using classifier combination

TL;DR: A lexicon free approach for the recognition of 3D handwritten words in Latin and Devanagari scripts by combining multiple classifiers by using the Recognizer Output Voting Error Reduction (ROVER) framework.
Posted Content

DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems

TL;DR: An in-depth evaluation on a state-of-the-art speech-to-text DL system demonstrates the effectiveness of the model RNN as an abstract state transition system in improving quality and reliability of stateful DL systems.
Proceedings ArticleDOI

A sequence learning approach for multiple script identification

TL;DR: A novel methodology for multiple script identification using Long Short-Term Memory networks' sequence-learning capabilities, which is able to identify multiple scripts at text-line level, where two or more scripts are present in the same text- line.
Proceedings ArticleDOI

Handwriting Recognition by Attribute Embedding and Recurrent Neural Networks

TL;DR: This paper proposes a handwriting recognition method that adapts the attribute embedding to sequence learning and obtains promising results even without the use of any kind of dictionary or language model.
Proceedings ArticleDOI

Connectionist Temporal Modeling of Video and Language: a Joint Model for Translation and Sign Labeling.

TL;DR: A Connectionist Temporal Modeling (CTM) network for sentence translation and sign labeling is proposed and dynamic programming is embedded into the decoding scheme, which learns temporal mapping among features, sign labels, and the generated sentence directly.
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.
Related Papers (5)