51 research outputs found
Curriculum Learning for Handwritten Text Line Recognition
Recurrent Neural Networks (RNN) have recently achieved the best performance
in off-line Handwriting Text Recognition. At the same time, learning RNN by
gradient descent leads to slow convergence, and training times are particularly
long when the training database consists of full lines of text. In this paper,
we propose an easy way to accelerate stochastic gradient descent in this
set-up, and in the general context of learning to recognize sequences. The
principle is called Curriculum Learning, or shaping. The idea is to first learn
to recognize short sequences before training on all available training
sequences. Experiments on three different handwritten text databases (Rimes,
IAM, OpenHaRT) show that a simple implementation of this strategy can
significantly speed up the training of RNN for Text Recognition, and even
significantly improve performance in some cases
The Claire French Dialogue Dataset
We present the Claire French Dialogue Dataset (CFDD), a resource created by
members of LINAGORA Labs in the context of the OpenLLM France initiative. CFDD
is a corpus containing roughly 160 million words from transcripts and stage
plays in French that we have assembled and publicly released in an effort to
further the development of multilingual, open source language models. This
paper describes the 24 individual corpora of which CFDD is composed and
provides links and citations to their original sources. It also provides our
proposed breakdown of the full CFDD dataset into eight categories of subcorpora
and describes the process we followed to standardize the format of the final
dataset. We conclude with a discussion of similar work and future directions
A Novel Strategy for Speaker Verification based on SVM Classification of Pairs of Speech Sequence
International audienc
Curriculum learning for handwritten text line recognition. preprint arXiv:1312.1737
Abstract-Recurrent Neural Networks (RNN) have recently achieved the best performance in off-line Handwriting Text Recognition. At the same time, learning RNN by gradient descent leads to slow convergence, and training times are particularly long when the training database consists of full lines of text. In this paper, we propose an easy way to accelerate stochastic gradient descent in this set-up, and in the general context of learning to recognize sequences. The principle is called Curriculum Learning, or shaping. The idea is to first learn to recognize short sequences before training on all available training sequences. Experiments on three different handwritten text databases (Rimes, IAM, OpenHaRT) show that a simple implementation of this strategy can significantly speed up the training of RNN for Text Recognition, and even significantly improve performance in some cases
Pair-of-Sequences SVM Speaker Verification
International audienc
State-Of-The-Art Sequence Kernels For SVM Speaker Verification
International audienc
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