Abstract Score normalization is an essential part for a spoken term detection (STD) system. In this paper, a two-stage score normalization method is proposed. Firstly, two features, rank-p and relative-to-max, are introduced into a discriminative score normalization method to get more discriminative confidence scores between correct and wrong candidate words, and thus the keyword verification is more accurate. Secondly, a term-weighted value evaluation metric based normalization method is applied to further optimize the performance of STD. Experimental results show that the proposed method takes advantages of both discrimination and metric-based score normalization methods, and it obtains better performance than the best single score normalization method does.
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2016, Vol. 29 
