A new look at discriminative training for hidden Markov models
Pattern Recognition Letters
Multi-candidate reduction: Sentence compression as a tool for document summarization tasks
Information Processing and Management: an International Journal
Invited paper: Automatic speech recognition: History, methods and challenges
Pattern Recognition
Using Skipping for Sequence-Based Collaborative Filtering
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Phrase-based correction model for improving handwriting recognition accuracies
Pattern Recognition
Efficient backward decoding of high-order hidden Markov models
Pattern Recognition
Zero knowledge hidden Markov model inference
Pattern Recognition Letters
Joint evaluation of multiple speech patterns for speech recognition and training
Computer Speech and Language
Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Estimation of stochastic context-free grammars and their use as language models
Computer Speech and Language
Unsupervized data-driven partitioning of multiclass problems
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
Parallel computer workload modeling with markov chains
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Performance of a SCFG-based language model with training data sets of increasing size
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
CEU-UPV English-Spanish system for WMT11
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
A hybrid approach to statistical language modeling with multilayer perceptrons and unigrams
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
Phoneme based acoustics keyword spotting in informal continuous speech
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
Language models for machine translation: original vs. translated texts
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A survey of techniques for incremental learning of HMM parameters
Information Sciences: an International Journal
On the assessment of text corpora
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
Frequentist and bayesian approach to information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
The latent words language model
Computer Speech and Language
Bayesian classification of Hidden Markov Models
Mathematical and Computer Modelling: An International Journal
Development of the 2012 SJTU HVR system
Proceedings of the 14th ACM international conference on Multimodal interaction
Revisiting the case for explicit syntactic information in language models
WLM '12 Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT
Language models for machine translation: Original vs. translated texts
Computational Linguistics
Expert Systems with Applications: An International Journal
Human sensing for smart cities
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Semantic spaces for improving language modeling
Computer Speech and Language
Disambiguation of imprecise input with one-dimensional rotational text entry
ACM Transactions on Computer-Human Interaction (TOCHI)
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Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the parameters for such models from sparse data. We also describe two decoding methods, one appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks. To illustrate the usefulness of the methods described, we review a number of decoding results that have been obtained with them.