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SRILM -- An extensible language modeling toolkit

by Andreas Stolcke - IN PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING (ICSLP 2002 , 2002
"... 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 ..."
Abstract - Cited by 1218 (21 self) - Add to MetaCart
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

Building a Large Annotated Corpus of English: The Penn Treebank

by Mitchell P. Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz - COMPUTATIONAL LINGUISTICS , 1993
"... There is a growing consensus that significant, rapid progress can be made in both text understanding and spoken language understanding by investigating those phenomena that occur most centrally in naturally occurring unconstrained materials and by attempting to automatically extract information abou ..."
Abstract - Cited by 2740 (10 self) - Add to MetaCart
for enterprises as diverse as the automatic construction of statistical models for the grammar of the written and the colloquial spoken language, the development of explicit formal theories of the differing grammars of writing and speech, the investigation of prosodic phenomena in speech, and the evaluation

Hidden Markov models for detecting remote protein homologies

by Kevin Karplus, Christian Barrett, Richard Hughey - Bioinformatics , 1998
"... A new hidden Markov model method (SAM-T98) for nding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (hmm) from the sequence and homologs found using the hmm for database search. SAM-T98 is ..."
Abstract - Cited by 462 (15 self) - Add to MetaCart
is also used to construct model libraries automatically from sequences in structural databases. We evaluate the SAM-T98 method with four datasets. Three of the test sets are fold-recognition tests, where the correct answers are determined by structural similarity. The fourth uses a curated database

A Post-Processing System To Yield Reduced Word Error Rates: Recognizer Output Voting Error Reduction (ROVER)

by Jonathan G. Fiscus , 1997
"... This paper describes a system 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, in many cases, the composite ASR output has lower error rate than any of the individual systems. The system i ..."
Abstract - Cited by 422 (2 self) - Add to MetaCart
This paper describes a system 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, in many cases, the composite ASR output has lower error rate than any of the individual systems. The system

Comparison of Four Approaches to Automatic Language Identification of Telephone Speech

by Marc A. Zissman , 1996
"... We have compared the performance of four approaches for automatic language identification of speech utterances: Gaussian mixture model (GMM) classification; single-language phone recognition followed by language-dependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses mul ..."
Abstract - Cited by 198 (1 self) - Add to MetaCart
We have compared the performance of four approaches for automatic language identification of speech utterances: Gaussian mixture model (GMM) classification; single-language phone recognition followed by language-dependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses

The Hierarchical Hidden Markov Model: Analysis and Applications

by Shai Fine, Yoram Singer - MACHINE LEARNING , 1998
"... . We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our model is motivated by the complex multi-scale structure which appears in many natural sequences, particularly in langua ..."
Abstract - Cited by 326 (3 self) - Add to MetaCart
in language, handwriting and speech. We seek a systematic unsupervised approach to the modeling of such structures. By extendingthe standard forward-backward(BaumWelch) algorithm, we derive an efficient procedure for estimating the model parameters from unlabeled data. We then use the trained model

Graphical models and automatic speech recognition

by Jeffrey A. Bilmes - Mathematical Foundations of Speech and Language Processing , 2003
"... Graphical models provide a promising paradigm to study both existing and novel techniques for automatic speech recognition. This paper first provides a brief overview of graphical models and their uses as statistical models. It is then shown that the statistical assumptions behind many pattern recog ..."
Abstract - Cited by 78 (15 self) - Add to MetaCart
. Additionally, this paper includes a novel graphical analysis regarding why derivative (or delta) features improve hidden Markov model-based speech recognition by improving structural discriminability. It also includes an example where a graph can be used to represent language model smoothing constraints

Exploiting syntactic structure for language modeling

by Ciprian Chelba, Frederick Jelinek - In Proc. of COLING-ACL , 1998
"... The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words–binary-parse-structure with headword annotati ..."
Abstract - Cited by 138 (5 self) - Add to MetaCart
annotation and operates in a left-to-right manner — therefore usable for automatic speech recognition. The model, its probabilistic parameterization, and a set of experiments meant to evaluate its predictive power are presented; an improvement over standard trigram modeling is achieved. 1

Prosody-based automatic segmentation of speech into sentences and topics

by Elizabeth Shriberg, Andreas Stolcke, Dilek Hakkani-Tür, Gökhan Tür - SPEECH COMMUNICATION , 2000
"... A crucial step in processing speech audio data for information-extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are abse ..."
Abstract - Cited by 205 (46 self) - Add to MetaCart
) are absent in spoken language. We investigate the use of prosody (informationgleaned from the timing and melody of speech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast

Learning words from sights and sounds: a computational model

by Deb K. Roy, Alex P. Pentland , 2002
"... This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross-modal structure. The model has been imple ..."
Abstract - Cited by 270 (31 self) - Add to MetaCart
implemented in a system using novel speech processing, computer vision, and machine learning algorithms. In evaluations the model successfully performed speech segmentation, word discovery and visual categorization from spontaneous infant-directed speech paired with video images of single objects
Results 1 - 10 of 2,946