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This paper presents an improved acoustic keyword spotting (KWS) algorithm using a novel confusion garbage model in Mandarin conversational speech. Observing the KWS corpus, we found there are many words with similar pronunciation with predefined keywords, although they have different Chinese characters and different
We present a handwritten text Keyword Spotting (KWS) approach based on the combination of KWS methods using word-graphs (WGs) and character-lattices (CLs). It aims to solve the problem that WG-based models present for out of vocabulary (OOV) keywords: since there is no available information about them in the lexicon
Document clustering is to group documents according to a certain semantic features defined on the document set for measuring the similarities between two documents. The keyword models such as the TFIDF model of document have been widely used as features for document clustering. But it lacks of semantic structure
Automatic annotating images by equipment is of great interest as it meets one's common need for retrieving image content. Usually image content description with keywords is regarded as a visual-word correlation process. However, in view of the viewer's psychology, image to words is a kind of cognition process, which
Candidates routinely use a set of key phrases or keywords to succinctly describe their expertise or skillset. This is useful for both matching candidate profiles to jobs and for comparing different candidates. Constant development of businesses and labour market has dynamic impact on importance of such skills, where
. Through quantifiable index analysis, statistical language model analysis, automatic keywords extraction and application of other technologies and theoretical models, the three progressive tiers, namely non-semantic tier, semantic tier and relevant behavior tier, were induced, thus the Three-tier Criteria Based Essay Rating
In this paper, we propose a novel system for word spotting and regular expression detection in Handwritten documents. The proposed approach is lexicon-free, i.e., able to spot arbitrary keywords that are not required to be known at the training stage. Furthermore, the proposed system is segmentation-free, i.e., text
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