KEY-PHRASE SPOTTING USING AN INTEGRATED LANGUAGE MODEL OF N-GRAMS AND FINITE-STATE GRAMMAR

Abstract

This paper describes a new algorithm for key-phrase spotting applications. The algorithm consists of three processes. The first process is to synergistically integrate N-grams with Finite-State Grammars (FSG) - the two conventional language models (LM) for speech recognition. All the key phrases to be spotted are covered by the FSG component of the recognizer's LM, while the N-grams are used for decoding surrounding non-key phrases. Secondly, selective weighting is proposed and implemented. The weighting parameters independently control the triggering and completion of FSG on top of N-grams. Finally, the third process involves a word confirmation and rejection logic which determines whether to accept or reject a hypothesized key phrase. The proposed algorithm has been favorably evaluated on two separate experiments. In these experiments, only the FSG part of the LM need be updated for different application tasks while the N-gram part can remain unchanged.