Mandarin keyword spotting using syllable based confidence features and SVM

A method for confidence measure (CM) using syllable based confidence features is proposed to improve false-alarm rejection of the mandarin keyword spotting (KWS). The features take advantage of the merit of mandarin syllable structure and describe the confidences in every sub-syllable level. The evaluation is processed with support vector machine (SVM) on telephone speech database. Compared with the typical method, the experimental results show that the proposed CM features and SVM based method yields significant improvement, and at best a reduction of 12.13% equal error rate (EER) is gotten.