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In this paper we report our approaches to accomplishing the very limited resource keyword search (KWS) task in the NIST Open Keyword Search 2015 (OpenKWS15) Evaluation. We devised the methods, first, to attain better acoustic modeling, multilingual and semi-supervised acoustic model training as well as the examplar
images automatically. Cluster IDs are adopted to index the characters. A Dream of Red Mansions, a famous classical Chinese literature work including near one million characters, is used to evaluate the performance of Chinese keyword spotting. Experimental results confirm the effectiveness of knowledge-based clustering and
. This paper presents an initial study using n-best recognition hypotheses for two tasks, extractive summarization and keyword extraction. We extend the approach used on 1-best output to n-best hypotheses: MMR (maximum marginal relevance) for summarization and TFIDF (term frequency, inverse document frequency) weighting for
Two keyword-extraction ways are usually used, one is simply using the information from exactly single word like word frequency and TF.IDF, the other is based on the relationship between words. The relationship is usually described as word similarity which derives from a corpus (WordNet, HowNet) or man-made thesaurus
Although keyword spotting (KWS) technologies have been successfully applied to some applications, most KWS systems have a common problem of noise-robustness when applied to real-world environments. Audio-visual keyword spotting (AVKWS) using both acoustic and visual information is a solution to complementarily solve
In this paper, we propose a novel image search scheme is contextual image search with keyword input. It is different from conventional image search schemes. it consist of three step process, first one is context extraction to distinguish the image entities of the same name, second step is conceptualization to convert
Online advertising has now turned to be one of the major revenue sources for today's Internet companies. Among the different channels of advertising, contextual advertising takes the great part. There are already lots of studies done for the keyword extraction problem in contextual advertising for English, however
by the network -- descriptive keywords, or tags. In this paper we present a model that enables keyword discovery methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid
This paper proposes a new keyword extraction method that uses bag-of-concept to extract keywords from Arabic text. The proposed algorithm utilizes semantic vector space model instead of traditional vector space model to group words into classes. The new method built word-context matrix where the synonym words will be
The search engine, keyword extraction is an important technique. In this paper, aiming at the defects of the traditional keyword extraction algorithm, we proposed an improved weight computation strategy. The experimental results show that, the improved method's results are significantly better results than the
This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously
In this paper, we present a keyword extraction methodology from handwritten Chinese document image based on matching and voting of the local topological structure. In the process, a handwritten keyword image is used as template, from which the local topological structure features of each character pixel are extracted
In this paper we describe the 2016 BBN conversational telephone speech keyword spotting system; the culmination of four years of research and development under the IARPA Babel program. The system was constructed in response to the NIST Open Keyword Search (OpenKWS) evaluation of 2016. We present our technological
This paper proposes a Bag of Visual Words (BoVW) based approach for keyword spotting on the Mongolian historical document images. In this paper, the first step is dividing the scanned Mongolian historical document images into word images by some preprocessing steps, such as connected component analysis, binarization
Keyword spotting remains a challenge when applied to real-world environments with dramatically changing noise. In recent studies, audio-visual integration methods have demonstrated superiorities since visual speech is not influenced by acoustic noise. However, for visual speech recognition, individual utterance
Word posterior probability has been widely used as the confidence estimation of automatic speech recognition (ASR) systems and has been proved to be quite effective in related applications such as keyword search. However, word posterior probability tends to overestimate the true probability of a hypothesis, as it is
A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite
integrated feature set is obtained after normalization of both sets of features thus obtained. This integrated feature set is used in a Hidden Markov Modeling (HMM) framework along with a novel sliding syllable protocol for keyword spotting. Keyword spotting experiments are conducted on the Hindi language database developed for
We use query-by-example keyword spotting (QbyE-KWS) approach to solve the personalized wake-up word detection problem for small-footprint, low-computational cost on-device applications. QbyE-KWS takes keywords as templates, and matches the templates across an audio stream via DTW to see if the keyword is included. In
In this paper, we introduced language network and described three kinds of networks. Keyword extraction is an important technology in many areas of document processing. In particularly, a keyword extraction algorithm based on language network and PageRank is proposed. Firstly a semantic network for a single document
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