On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Discriminative language modeling with conditional random fields and the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Discriminative syntactic language modeling for speech recognition
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discriminative n-gram language modeling
Computer Speech and Language
Corrective models for speech recognition of inflected languages
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error
IEEE Transactions on Audio, Speech, and Language Processing
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This paper focuses on discriminative language models (DLMs) for large vocabulary speech recognition tasks. To train such models, we usually use a large number of hypotheses generated for each utterance by a speech recognizer, namely an n-best list or a lattice. Since the data size is large, we usually need a high-end machine or a large-scale distributed computation system consisting of many computers for model training. However, it is still unclear whether or not such a large number of sentence hypotheses are necessary. Furthermore, we do not know which kinds of sentences are necessary. In this paper, we show that we can generate a high performance model using small subsets of the n-best lists by choosing samples properly, i.e., we describe a sample selection method for DLMs. Sample selection reduces the memory footprint needed for holding training samples and allows us to train models in a standard machine. Furthermore, it enables us to generate a highly accurate model using various types of features. Specifically, experimental results show that even training using two samples in each list can provide an accurate model with a small memory footprint.