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Class-Based n-gram Models of Natural Language
TLDR
We address the problem of predicting a word from previous words in a sample of text using n-gram models based on classes of words. Expand
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Maximum mutual information estimation of hidden Markov model parameters for speech recognition
TLDR
A method for estimating the parameters of hidden Markov models of speech is described. Expand
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A tree-based statistical language model for natural language speech recognition
TLDR
The problem of predicting the next word a speaker will say, given the words already spoken; is discussed. Expand
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Estimating hidden Markov model parameters so as to maximize speech recognition accuracy
TLDR
The problem of estimating the parameter values of hidden Markov word models for speech recognition is addressed. Expand
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A new algorithm for the estimation of hidden Markov model parameters
TLDR
We propose an alternative estimation procedure called corrective training which is aimed at minimizing the number of recognition errors. Expand
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Decision trees for phonological rules in continuous speech
TLDR
The authors present an automatic method for modeling phonological variation using decision trees. Expand
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Robust methods for using context-dependent features and models in a continuous speech recognizer
TLDR
In this paper we describe the method we use to derive acoustic features that reflect some of the dynamics of frame-based parameter vectors. Expand
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A method for the construction of acoustic Markov models for words
TLDR
A technique for constructing Markov models for the acoustic representation of words is described. Expand
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A statistical approach to the design of an adaptive self-normalizing silence detector
TLDR
Silence detection is an important aspect of automatic speech recognition. Expand
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