• Publications
  • Influence
A Systematic Comparison of Various Statistical Alignment Models
TLDR
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. Expand
Improved Statistical Alignment Models
TLDR
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. Expand
LSTM Neural Networks for Language Modeling
TLDR
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. Expand
HMM-Based Word Alignment in Statistical Translation
TLDR
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. Expand
Joint-sequence models for grapheme-to-phoneme conversion
TLDR
A novel estimation algorithm is presented that demonstrates high accuracy on a variety of databases and studies the impact of the maximum approximation in training and transcription, the interaction of model size parameters, n-best list generation, confidence measures, and phoneme-to-grapheme conversion. Expand
The Alignment Template Approach to Statistical Machine Translation
  • F. Och, H. Ney
  • Computer Science
  • Computational Linguistics
  • 1 December 2004
TLDR
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. Expand
Improved backing-off for M-gram language modeling
TLDR
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. Expand
Discriminative Training and Maximum Entropy Models for Statistical Machine Translation
TLDR
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. Expand
Confidence measures for large vocabulary continuous speech recognition
TLDR
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. Expand
Features for image retrieval: an experimental comparison
TLDR
An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented and the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications. Expand
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