Recent advances in phonotactic language recognition using binary-decision trees

Abstract

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.