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We propose two simple methods to improve the performance of a keyword spotting system. In our application, the users are allowed to change the keywords anytime if they want. Thus we focused on phone-based GMM-HMM models since they do not require keyword-specific training data. However, the GMM-HMM based models usually
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
The so-called filler or garbage Hidden Markov Models (HMM) are among the most widely used models for lexicon-free, query by string key word spotting in the fields of speech recognition and (lately) handwritten text recognition. An important drawback of this approach is the large computational cost of the keyword
actual language identification. On our bi-lingual lecture tasks the PPRLM system clearly outperforms the PPR system in various segment length conditions, however at the cost of slower run-time. By using lexical information in the form of keyword spotting, and additional language models we show ways to improve the
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
Pattern searching and retrieval plays important role in task of content-based audio analysis for requirements of media database management or in surveillance systems for detecting significant audio events and keywords. In the paper, we present algorithm for spotting audio patterns in record, using Hidden Markov Models
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