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Stehr, in: International Workshop on Intelligent Transportation (WIT 2011), 2011.","mla":"Bevermeier, Maik, et al. “A Platform for Efficient Supply Chain Management Support in Logistics.” <i>International Workshop on Intelligent Transportation (WIT 2011)</i>, 2011.","bibtex":"@inproceedings{Bevermeier_Flanke_Haeb-Umbach_Stehr_2011, title={A Platform for efficient Supply Chain Management Support in Logistics}, booktitle={International Workshop on Intelligent Transportation (WIT 2011)}, author={Bevermeier, Maik and Flanke, Stephan and Haeb-Umbach, Reinhold and Stehr, Jan}, year={2011} }","ama":"Bevermeier M, Flanke S, Haeb-Umbach R, Stehr J. A Platform for efficient Supply Chain Management Support in Logistics. In: <i>International Workshop on Intelligent Transportation (WIT 2011)</i>. ; 2011."},"type":"conference","oa":"1","department":[{"_id":"54"}],"date_created":"2019-07-12T05:26:58Z"},{"date_updated":"2022-01-06T06:51:08Z","status":"public","title":"Uncertainty Decoding and Conditional Bayesian Estimation","year":"2011","author":[{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold","id":"242"}],"user_id":"44006","editor":[{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"},{"last_name":"Kolossa","first_name":"Dorothea","full_name":"Kolossa, Dorothea"}],"publisher":"Springer","_id":"11774","language":[{"iso":"eng"}],"abstract":[{"text":"In this contribution classification rules for HMM-based speech recognition in the presence of a mismatch between training and test data are presented. The observed feature vectors are regarded as corrupted versions of underlying and unobservable clean feature vectors, which have the same statistics as the training data. Optimal classification then consists of two steps. First, the posterior density of the clean feature vector, given the observed feature vectors, has to be determined, and second, this posterior is employed in a modified classification rule, which accounts for imperfect estimates. We discuss different variants of the classification rule and further elaborate on the estimation of the clean speech feature posterior, using conditional Bayesian estimation. It is shown that this concept is fairly general and can be applied to different scenarios, such as noisy or reverberant speech recognition.","lang":"eng"}],"publication":"Robust Speech Recognition of Uncertain or Missing Data","citation":{"ama":"Haeb-Umbach R. Uncertainty Decoding and Conditional Bayesian Estimation. In: Haeb-Umbach R, Kolossa D, eds. <i>Robust Speech Recognition of Uncertain or Missing Data</i>. Springer; 2011.","bibtex":"@inbook{Haeb-Umbach_2011, title={Uncertainty Decoding and Conditional Bayesian Estimation}, booktitle={Robust Speech Recognition of Uncertain or Missing Data}, publisher={Springer}, author={Haeb-Umbach, Reinhold}, editor={Haeb-Umbach, Reinhold and Kolossa, DorotheaEditors}, year={2011} }","mla":"Haeb-Umbach, Reinhold. “Uncertainty Decoding and Conditional Bayesian Estimation.” <i>Robust Speech Recognition of Uncertain or Missing Data</i>, edited by Reinhold Haeb-Umbach and Dorothea Kolossa, Springer, 2011.","chicago":"Haeb-Umbach, Reinhold. “Uncertainty Decoding and Conditional Bayesian Estimation.” In <i>Robust Speech Recognition of Uncertain or Missing Data</i>, edited by Reinhold Haeb-Umbach and Dorothea Kolossa. Springer, 2011.","short":"R. Haeb-Umbach, in: R. Haeb-Umbach, D. Kolossa (Eds.), Robust Speech Recognition of Uncertain or Missing Data, Springer, 2011.","apa":"Haeb-Umbach, R. (2011). Uncertainty Decoding and Conditional Bayesian Estimation. In R. Haeb-Umbach &#38; D. Kolossa (Eds.), <i>Robust Speech Recognition of Uncertain or Missing Data</i>. Springer.","ieee":"R. Haeb-Umbach, “Uncertainty Decoding and Conditional Bayesian Estimation,” in <i>Robust Speech Recognition of Uncertain or Missing Data</i>, R. Haeb-Umbach and D. Kolossa, Eds. Springer, 2011."},"type":"book_chapter","department":[{"_id":"54"}],"date_created":"2019-07-12T05:28:00Z"}]