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tech,12-2-H92-1036,bq	The classical <term> MLE reestimation algorithms </term> , namely the <term> forward-backward algorithm </term> and the <term> segmental k-means algorithm </term> , are expanded and <term> reestimation formulas </term> are given for <term> HMM with Gaussian mixture observation densities </term> .	#19080 The classical MLE reestimation algorithms, namely the forward-backward algorithm and thesegmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities.
tech,2-2-H92-1036,bq	The classical <term> MLE reestimation algorithms </term> , namely the <term> forward-backward algorithm </term> and the <term> segmental k-means algorithm </term> , are expanded and <term> reestimation formulas </term> are given for <term> HMM with Gaussian mixture observation densities </term> .	#19070 The classicalMLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities.
tech,22-3-H92-1036,bq	Because of its adaptive nature , <term> Bayesian learning </term> serves as a unified approach for the following four <term> speech recognition </term> applications , namely <term> parameter smoothing </term> , <term> speaker adaptation </term> , <term> speaker group modeling </term> and <term> corrective training </term> .	#19121 Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namelyparameter smoothing, speaker adaptation, speaker group modeling and corrective training.
tech,25-3-H92-1036,bq	Because of its adaptive nature , <term> Bayesian learning </term> serves as a unified approach for the following four <term> speech recognition </term> applications , namely <term> parameter smoothing </term> , <term> speaker adaptation </term> , <term> speaker group modeling </term> and <term> corrective training </term> .	#19124 Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing,speaker adaptation, speaker group modeling and corrective training.
model,24-2-H92-1036,bq	The classical <term> MLE reestimation algorithms </term> , namely the <term> forward-backward algorithm </term> and the <term> segmental k-means algorithm </term> , are expanded and <term> reestimation formulas </term> are given for <term> HMM with Gaussian mixture observation densities </term> .	#19092 The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given forHMM with Gaussian mixture observation densities.
tech,15-4-H92-1036,bq	New experimental results on all four applications are provided to show the effectiveness of the <term> MAP estimation approach </term> .	#19149 New experimental results on all four applications are provided to show the effectiveness of theMAP estimation approach.
tech,32-3-H92-1036,bq	Because of its adaptive nature , <term> Bayesian learning </term> serves as a unified approach for the following four <term> speech recognition </term> applications , namely <term> parameter smoothing </term> , <term> speaker adaptation </term> , <term> speaker group modeling </term> and <term> corrective training </term> .	#19131 Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling andcorrective training.
model,7-1-H92-1036,bq	We discuss <term> maximum a posteriori estimation </term> of <term> continuous density hidden Markov models ( CDHMM ) </term> .	#19059 We discuss maximum a posteriori estimation ofcontinuous density hidden Markov models ( CDHMM ).
tech,28-3-H92-1036,bq	Because of its adaptive nature , <term> Bayesian learning </term> serves as a unified approach for the following four <term> speech recognition </term> applications , namely <term> parameter smoothing </term> , <term> speaker adaptation </term> , <term> speaker group modeling </term> and <term> corrective training </term> .	#19127 Because of its adaptive nature, Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation,speaker group modeling and corrective training.
tech,8-2-H92-1036,bq	The classical <term> MLE reestimation algorithms </term> , namely the <term> forward-backward algorithm </term> and the <term> segmental k-means algorithm </term> , are expanded and <term> reestimation formulas </term> are given for <term> HMM with Gaussian mixture observation densities </term> .	#19076 The classical MLE reestimation algorithms, namely theforward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities.
tech,17-3-H92-1036,bq	Because of its adaptive nature , <term> Bayesian learning </term> serves as a unified approach for the following four <term> speech recognition </term> applications , namely <term> parameter smoothing </term> , <term> speaker adaptation </term> , <term> speaker group modeling </term> and <term> corrective training </term> .	#19116 Because of its adaptive nature, Bayesian learning serves as a unified approach for the following fourspeech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training.
other,19-2-H92-1036,bq	The classical <term> MLE reestimation algorithms </term> , namely the <term> forward-backward algorithm </term> and the <term> segmental k-means algorithm </term> , are expanded and <term> reestimation formulas </term> are given for <term> HMM with Gaussian mixture observation densities </term> .	#19087 The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded andreestimation formulas are given for HMM with Gaussian mixture observation densities.
tech,2-1-H92-1036,bq	We discuss <term> maximum a posteriori estimation </term> of <term> continuous density hidden Markov models ( CDHMM ) </term> .	#19054 We discussmaximum a posteriori estimation of continuous density hidden Markov models (CDHMM).
tech,6-3-H92-1036,bq	Because of its adaptive nature , <term> Bayesian learning </term> serves as a unified approach for the following four <term> speech recognition </term> applications , namely <term> parameter smoothing </term> , <term> speaker adaptation </term> , <term> speaker group modeling </term> and <term> corrective training </term> .	#19105 Because of its adaptive nature,Bayesian learning serves as a unified approach for the following four speech recognition applications, namely parameter smoothing, speaker adaptation, speaker group modeling and corrective training.


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