Open AccessProceedings Article
SRILM – An Extensible Language Modeling Toolkit
TLDR
The functionality of the SRILM toolkit is summarized and its design and implementation is discussed, highlighting ease of rapid prototyping, reusability, and combinability of tools.Abstract:
SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and experimentation with statistical language models for speech recognition and other applications. SRILM is freely available for noncommercial purposes. The toolkit supports creation and evaluation of a variety of language model types based on N-gram statistics, as well as several related tasks, such as statistical tagging and manipulation of N-best lists and word lattices. This paper summarizes the functionality of the toolkit and discusses its design and implementation, highlighting ease of rapid prototyping, reusability, and combinability of tools.read more
Citations
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Book
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
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TL;DR: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora, to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation.
References
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Book
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Dan Jurafsky,James Martin +1 more
TL;DR: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora, to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation.
Journal ArticleDOI
Class-based n -gram models of natural language
Peter Fitzhugh Brown,Peter Vincent Desouza,Robert Leroy Mercer,Vincent J. Della Pietra,Jenifer C. Lai +4 more
TL;DR: This work addresses the problem of predicting a word from previous words in a sample of text and discusses n-gram models based on classes of words, finding that these models are able to extract classes that have the flavor of either syntactically based groupings or semanticallybased groupings, depending on the nature of the underlying statistics.
Journal ArticleDOI
An empirical study of smoothing techniques for language modeling
TL;DR: This work surveys the most widely-used algorithms for smoothing models for language n -gram modeling, and presents an extensive empirical comparison of several of these smoothing techniques, including those described by Jelinek and Mercer (1980), and introduces methodologies for analyzing smoothing algorithm efficacy in detail.
Proceedings ArticleDOI
A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)
TL;DR: The NIST Recognizer Output Voting Error Reduction (ROVER) system as discussed by the authors was developed at NIST to produce a composite automatic speech recognition (ASR) system output when the outputs of multiple ASR systems are available, and for which the composite ASR output has a lower error rate than any of the individual systems.
Journal ArticleDOI
Finding consensus in speech recognition: word error minimization and other applications of confusion networks☆
TL;DR: A new framework for distilling information from word lattices is described to improve the accuracy of the speech recognition output and obtain a more perspicuous representation of a set of alternative hypotheses.