Self-organized language modeling for speech recognition
Readings in speech recognition
New results with the Lincoln tied-mixture HMM CSR system
HLT '91 Proceedings of the workshop on Speech and Natural Language
BYBLOS speech recognition benchmark results
HLT '91 Proceedings of the workshop on Speech and Natural Language
Algorithms for an optimal A search and linearizing the search in the stack decoder
HLT '90 Proceedings of the workshop on Speech and Natural Language
The Lincoln tied-mixture HMM continuous speech recognizer
HLT '90 Proceedings of the workshop on Speech and Natural Language
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Automatic Speech Recognition: The Development of the Sphinx Recognition System
A simple statistical class grammar for measuring speech recognition performance
HLT '89 Proceedings of the workshop on Speech and Natural Language
HLT '89 Proceedings of the workshop on Speech and Natural Language
A stack decoder for continous speech recognition
HLT '89 Proceedings of the workshop on Speech and Natural Language
A CSR-NL interface specification version 1.5
HLT '89 Proceedings of the workshop on Speech and Natural Language
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
New results with the Lincoln tied-mixture HMM CSR system
HLT '91 Proceedings of the workshop on Speech and Natural Language
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
Similarity-based estimation of word cooccurrence probabilities
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
The design for the wall street journal-based CSR corpus
HLT '91 Proceedings of the workshop on Speech and Natural Language
The Lincoln large-vocabulary HMM CSR
HLT '91 Proceedings of the workshop on Speech and Natural Language
The Lincoln large-vocabulary stack-decoder based HMM CSR
HLT '94 Proceedings of the workshop on Human Language Technology
The Lincoln large-vocabulary stack-decoder HMM CSR
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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Stochastic language models are more useful than non-stochastic models because they contribute more information than a simple acceptance or rejection of a word sequence. Back-off N-gram language models [11] are an effective class of word based stochastic language model. The first part of this paper describes our experiences using the back-off language models in our time-synchronous decoder CSR. A bigram back-off language model was chosen for the language model to be used in the informal ATIS CSR baseline evaluation test[13, 21].The stack decoder[2, 8, 24] is a promising control structure for a speech understanding system because it can combine constraints from both the acoustic model and a long span language model (such as a natural language processor (NLP)) into a single integrated search[17]. A copy of the Lincoln time-synchronous HMM CSR has been converted to a stack decoder controlled search with stochastic language models. The second part of this paper describes our experiences with our prototype stack decoder CSR using no grammar, the word-pair grammar, and N-gram back-off language models.