Improved Modeling of Cross-Decoder Phone Co-occurrences in SVM-based Phonotactic Language Recognition

摘要

Most common approaches to phonotactic language recognition deal with several independent phone decod-ings. These decodings are processed and scored in a fully uncoupled way, their time alignment (and the information that may be extracted from it) being completely lost. Recently, a new approach to phonotactic language recognition has been presented [1], which takes into account time alignment information, by considering cross-decoder phone co-occurrences at the frame level, under two language modeling paradigms: smoothed n-grams and Support Vector Machines (SVM). Experiments on the NIST LRE2007 database demonstrated that using phone co-occurrence statistics could improve the performance of baseline phonotactic recognizers. In this paper, two variants of the cross-decoder phone co-occurrence SVM-based approach are proposed, by considering: (1) n-grams (up to 3-grams) of phone co-occurrences; and (2) co-occurrences of phone n-grams (up to 3-grams). To evaluate these approaches, a choice of open software (Brno University of Technology phone decoders, LIB-LINEAR and FoCal) was used, and experiments were carried out on the NIST LRE2007 database. Unlike those presented in [1], the two approaches presented in this paper outperformed the baseline phonotactic system, yielding around 16% relative improvement in terms of EER. The best fused system attained a 1,88% EER (a 30% improvement with regard to the baseline system), which supports the use of cross-decoder dependencies for language modeling.