Sub-word modeling of out of vocabulary words in spoken term detection
ABSTRACT This paper deals with comparison of sub-word based methods for spoken term detection (STD) task and phone recognition. The sub-word units are needed for search for out-of-vocabulary words. We compared words, phones and multigrams. The maximal length and pruning of multigrams were investigated first. Then two constrained methods of multigram training were proposed. We evaluated on the NIST STD06 dev-set CTS data. The conclusion is that the proposed method improves the phone accuracy more than 9% relative and STD accuracy more than 7% relative.
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ABSTRACT: In this paper, we present an innovative web-based lecture browser. Its goal is to facilitate the access to information with the use of technologies for search in spoken documents. These technologies are first briefly presented, we continue with a description of software imple-mentation of the browser.
- INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, Makuhari, Chiba, Japan, September 26-30, 2010; 01/2010
Conference Paper: Spoken term detection for OOV terms based on triphone confusion matrix[Show abstract] [Hide abstract]
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).Chinese Spoken Language Processing (ISCSLP), 2012 8th International Symposium on; 01/2012