Spoken term detection for OOV terms based on triphone confusion matrix

Abstract

The search for out of vocabulary (OOV) query terms in spoken term detection (STD) task is addressed in this paper. The phone level fragment with word-position marker is naturally adopted as the speech recognition decoding unit. Then the triphone confusion matrix (TriCM) is used to expand the query space to compensate for speech recognition errors. And we also propose a new approach to construct triphone confusion matrix using a smoothing method similar with the Katz method to solve the data sparseness problem. Experimental result on the NIST STD06 eval-set conversational telephone speech (CTS) corpus indicates that triphone confusion matrix can provide a relative improvement of 12% in actual term weighted value (ATWV).