Discriminative boosting regression backend for phonotactic language recognition

In spoken language recognition (SLR), discriminatively trained models always outperform non-discriminative models but computationally expensive and complex to implement. In this paper, we explore a novel approach to discriminative vector space model (VSM) training by using a boosting regression framework, in which an ensemble of VSMs is trained sequentially. The effectiveness of our boosting variation comes from the emphasis on working with the high confidence test data to achieve discriminatively trained models. Our variant of boosting also includes utilizing original training data in VSM training. The discriminative boosting regression (DBR) is applied to the National Institute of Standards and Technology (NIST) language recognition evaluation (LRE) 2009 task and show performance improvements. The experimental results demonstrate that the proposed DBR shows 4.13%, 14.38% and 14.22% relative reduction for 30s, 10s and 3s test utterances in equal error rate (EER) than baseline system.