Adaptive boosted non-uniform mce for keyword spotting on spontaneous speech

Abstract

In this work, we present a complete framework of discriminative training using non-uniform criteria for keyword spotting, adaptive boosted non-uniform minimum classification error (MCE) for keyword spotting on spontaneous speech. To further boost the spotting performance and tackle the potential issue of over-training in the non-uniform MCE proposed in our prior work, we make two improvements to the fundamental MCE optimization procedure. Furthermore, motivated by AdaBoost, we introduce an adaptive scheme to embed error cost functions together with model combinations during the decoding stage. The proposed framework is comprehensively validated on two challenging large-scale spontaneous conversational telephone speech (CTS) tasks in different languages (English and Mandarin) and the experimental results show it can achieve significant and consistent figure of merit (FOM) gains over both ML and discriminatively trained systems.