A keyword-boosted sMBR criterion to enhance keyword search performance in deep neural network based acoustic modeling

We propose a keyword-boosted state-level minimum Bayes risk (sMBR) criterion for training DNN-HMM hybrid keyword search systems by enhancing acoustic detail of a given list of target keyword terms. The rationale behind the proposed discriminative training strategy is to place more acoustic modeling emphasis on states appearing in the given keywords. We observed a relative gain of 1.7 ~ 6.1% in actual term weighted value (ATWV) performance with the proposed keyword-boosted sMBR training over the conventional sMBR systems when tested on the IARPA Babel program's Vietnamese limited-language-pack task. A detailed result analysis suggests that the proposed sMBR objective function effectively improves the ATWV scores by boosting the probability of detecting keywords appearing in the system output with an increased correct and insertion rates in the decoded lattices.