| tech,12-2-H92-1036,bq | forward-backward algorithm </term> and the <term> | segmental k-means algorithm | </term> , are expanded and <term> reestimation | #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 | models ( CDHMM ) </term> . The classical <term> | MLE reestimation algorithms | </term> , namely the <term> forward-backward | #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 | recognition </term> applications , namely <term> | parameter smoothing | </term> , <term> speaker adaptation </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 | namely <term> parameter smoothing </term> , <term> | speaker adaptation | </term> , <term> speaker group modeling </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. |
| tech,15-4-H92-1036,bq | provided to show the effectiveness of the <term> | MAP estimation approach | </term> . It is well-known that there are | #19149 New experimental results on all four applications are provided to show the effectiveness of theMAP estimation approach. |
| tech,32-3-H92-1036,bq | <term> speaker group modeling </term> and <term> | corrective training | </term> . New experimental results on all | #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 | maximum a posteriori estimation </term> of <term> | continuous density hidden Markov models ( CDHMM ) | </term> . The classical <term> MLE reestimation | #19059 We discuss maximum a posteriori estimation ofcontinuous density hidden Markov models ( CDHMM ). |
| tech,28-3-H92-1036,bq | </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 | reestimation algorithms </term> , namely the <term> | forward-backward algorithm | </term> and the <term> segmental k-means algorithm | #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 | unified approach for the following four <term> | speech recognition | </term> applications , namely <term> parameter | #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 | algorithm </term> , are expanded and <term> | reestimation formulas | </term> are given for <term> HMM with Gaussian | #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 | % reduction in error . We discuss <term> | maximum a posteriori estimation | </term> of <term> continuous density hidden | #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 | #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. |
