{"id":155636,"date":"2002-09-01T00:00:00","date_gmt":"2002-09-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/mutual-information-phone-clustering-for-decision-tree-induction\/"},"modified":"2018-10-16T20:03:26","modified_gmt":"2018-10-17T03:03:26","slug":"mutual-information-phone-clustering-for-decision-tree-induction","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/mutual-information-phone-clustering-for-decision-tree-induction\/","title":{"rendered":"Mutual Information Phone Clustering for Decision Tree Induction"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The paper presents an automatic method for devising the question sets used for the induction of classification and regression trees. The algorithm employed is the well-known mutual information based bottom-up clustering applied to phone bigram statistics. The sets of phones at the nodes in the resulting binary tree are used as question sets for clustering context-sensitive (tri-phone) HMM output distributions in a large vocabulary speech recognizer. The algorithm is shown to perform as well and sometimes significantly better than question sets devised by human experts for a Spanish and German system evaluated on several tasks, respectively. It eliminates the need for linguistic expertise and it provides a faster solution as well.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper presents an automatic method for devising the question sets used for the induction of classification and regression trees. The algorithm employed is the well-known mutual information based bottom-up clustering applied to phone bigram statistics. 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