Journal ArticleDOI
An inequality for rational functions with applications to some statistical estimation problems
TLDR
The well-known Baum-Eagon inequality provides an effective iterative scheme for finding a local maximum for homogeneous polynomials with positive coefficients over a domain of probability values.Abstract:
The well-known Baum-Eagon inequality (1967) provides an effective iterative scheme for finding a local maximum for homogeneous polynomials with positive coefficients over a domain of probability values. However, in many applications the goal is to maximize a general rational function. In view of this, the Baum-Eagon inequality is extended to rational functions. Some of the applications of this inequality to statistical estimation problems are briefly described. >read more
Citations
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Journal ArticleDOI
Hidden Markov processes
Yariv Ephraim,Neri Merhav +1 more
TL;DR: An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented and consistency and asymptotic normality of the maximum-likelihood parameter estimator were proved under some mild conditions.
Proceedings ArticleDOI
Minimum Phone Error and I-smoothing for improved discriminative training
Daniel Povey,Philip C. Woodland +1 more
TL;DR: The Minimum Phone Error (MPE) and Minimum Word Error (MWE) criteria are smoothed approximations to the phone or word error rate respectively and I-smoothing which is a novel technique for smoothing discriminative training criteria using statistics for maximum likelihood estimation (MLE).
Journal ArticleDOI
Large scale discriminative training of hidden Markov models for speech recognition
Philip C. Woodland,Daniel Povey +1 more
TL;DR: It is shown that HMMs trained with MMIE benefit as much as MLE-trained HMMs from applying model adaptation using maximum likelihood linear regression (MLLR), which has allowed the straightforward integration of MMIe- trained HMMs into complex multi-pass systems for transcription of conversational telephone speech.
Proceedings ArticleDOI
Hidden conditional random fields for phone classification.
TL;DR: This paper presents the results on the TIMIT phone classification task and shows that HCRFs outperforms comparable ML and CML/MMI trained HMMs and has the ability to handle complex features without any change in training procedure.
Proceedings Article
Large Margin Hidden Markov Models for Automatic Speech Recognition
Fei Sha,Lawrence K. Saul +1 more
TL;DR: This work proposes a learning algorithm based on the goal of margin maximization in continuous density hidden Markov models for automatic speech recognition (ASR) using Gaussian mixture models, and obtains competitive results for phonetic recognition on the TIMIT speech corpus.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
Journal ArticleDOI
A Maximum Likelihood Approach to Continuous Speech Recognition
TL;DR: This paper describes a number of statistical models for use in speech recognition, with special attention to determining the parameters for such models from sparse data, and describes two decoding methods appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks.
Journal ArticleDOI
An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
Leonard E. Baum,J. A. Eagon +1 more
TL;DR: In this paper, a polynomial with nonnegative coefficients homogeneous of degree d in its variables is shown to be polynomially homogeneous unless 3(3(x))>P(x), where 3(x)=x.
Proceedings ArticleDOI
Maximum mutual information estimation of hidden Markov model parameters for speech recognition
TL;DR: A method for estimating the parameters of hidden Markov models of speech is described and recognition results are presented comparing this method with maximum likelihood estimation.
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