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Author: Jiri Navratil
Binary decision trees are an effective model structure in language recognition. This paper presents several related algorithmic steps to address data sparseness issues and computational complexity. In particular, a tree adaptation step, a recursive bottom-up smoothing step, and two variants of the Flip-Flop approximation algorithm are introduced to language detection and studied in the context of the NIST Language Recognition Evaluation task.
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