| 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 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 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,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. |
