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A Systematic Comparison of Various Statistical Alignment Models
An important result is that refined alignment models with a first-order dependence and a fertility model yield significantly better results than simple heuristic models.
LSTM Neural Networks for Language Modeling
This work analyzes the Long Short-Term Memory neural network architecture on an English and a large French language modeling task and gains considerable improvements in WER on top of a state-of-the-art speech recognition system.
Improved Statistical Alignment Models
It is shown that models with a first-order dependence and a fertility model lead to significantly better results than the simple models IBM-1 or IBM-2, which are not able to go beyond zero-order dependencies.
HMM-Based Word Alignment in Statistical Translation
- S. Vogel, H. Ney, C. Tillmann
- Computer ScienceInternational Conference on Computational…
- 5 August 1996
A new model for word alignment in statistical translation using a first-order Hidden Markov model for the word alignment problem as they are used successfully in speech recognition for the time alignment problem.
Improved backing-off for M-gram language modeling
This paper proposes to use distributions which are especially optimized for the task of back-off, which are quite different from the probability distributions that are usually used for backing-off.
Joint-sequence models for grapheme-to-phoneme conversion
The Alignment Template Approach to Statistical Machine Translation
A phrase-based statistical machine translation approach the alignment template approach is described, which allows for general many-to-many relations between words and is easier to extend than classical statistical machinetranslation systems.
Neural Sign Language Translation
- Necati Cihan Camgöz, Simon Hadfield, Oscar Koller, H. Ney, R. Bowden
- Computer Science, LinguisticsIEEE/CVF Conference on Computer Vision and…
- 1 June 2018
This work formalizes SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge) and allows to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language.
Discriminative Training and Maximum Entropy Models for Statistical Machine Translation
A framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source-channel approach as a special case and shows that a baseline statistical machinetranslation system is significantly improved using this approach.
Confidence measures for large vocabulary continuous speech recognition
- F. Wessel, R. Schlüter, Klaus Macherey, H. Ney
- Computer ScienceIEEE Transactions on Speech and Audio Processing
- 1 March 2001
It is shown that the posterior probabilities computed on word graphs outperform all other confidence measures and are compared with two alternative confidence measures, i.e., the acoustic stability and the hypothesis density.