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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
Deep learning had a significant impact on diverse pattern recognition tasks in the recent past. In this paper, we investigate its potential for keyword spotting in handwritten documents by designing a novel feature extraction system based on Convolutional Deep Belief Networks. Sliding window features are learned from
effective in terms of better precision. Proposed method makes use of keyword clusters for query expansion. Visual features are used for detecting duplicate images in proposed method. Removing duplicates leads to further improve in precision and recall in retrieval result
This paper fuses the techniques such as semantic network, the individuality service and agent, and references various research achievements of semantics Web on knowledge expression, RDF data manipulation and semantic retrieval, to propose an information retrieval model by combination of semantic with keyword based on
We study user-friendly voice interface to consumer electronics and propose a voice activation system that can make speech recognition activated only when voice sounds from legitimate users are detected. The proposed system enables efficient operation of speech recognition in a continuous listening environment without any touch and/or key input.
For text-query-based keyword spotting from handwritten Chinese documents, the index is usually organized as a candidate lattice to overcome the ambiguity of character segmentation. Each edge in the lattice denotes a candidate character associated with a candidate class. Character similarity (between character and
With the continued proliferation of location-based services, a growing number of web-accessible data objects are geotagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms
Spoken keyword recognition has been under the spotlight for the past several decades, but has gained significant attention in recent years due to the rapid increase in front-end technology applications for mobile and wearable computing. This work presents the trade-off in performance between Artificial Neural Networks
With the growing popularity of XML and emergence of streaming data model, processing streaming XML has become an important topic. This paper proposes keyword search solution over XML fragment streams based on hole-filler model. Two efficient indexes, dual list and sketch are developed to further improve the
In this paper we present the work done on social media analysis to predict civil unrest using keyword filtering. The information given on the social media is delivered to every person within the fraction of seconds. This rapid circulation of information and the people opinions through social platform affects or create
Document Summarization (ADS) systems are suitable for the task of outlining useful data. The ADS system model takes a text document as input, and outputs a semantically-relevant summary of this information. This information can be further separated and outlined as keywords, or keyphrases. This paper proposes a novel
Keyword search over relational databases (KSORD) enables casual users to use keyword queries (a set of keywords) to search relational databases just like searching the Web, without any knowledge of the database schema or any need of writing SQL queries. In KSORD, retrieval of user's initial query is often unsatisfying
The Bag-of-Visual-Words (BoVW) approach has been attracted some attention in the field of keyword spotting. However, the BoVW approach discards the spatial relations of the visual words. Therefore, a visual language model is integrated into the BoVW framework in this study so as to add the spatial information. To
Keyword search over databases, popularized by keyword search in WWW, allows ordinary users to access database information without the knowledge of structured query languages and database schemas. Most of the previous studies in this area use IR-style ranking, which fail to consider the importance of the query answers
, to generate answers to such questions. In this study, we report the evaluation of a new weighted-keyword model for improving our question answering system. As part of this development, a physician manually examined AskHERMES' answers to 20 ad hoc clinical questions created with and without the weighted-keyword model
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
One of the most important steps in a keyword spotting (KWS) system is a post-processing procedure to compute a confidence measure (CM) for each hypothesized keyword. The CM is commonly estimated by likelihood-based acoustic scores. However durations of the detected keyword, which include useful information, has not
Keyword extraction aims to find representative phrases for a document. Graph-based keyword extraction represent the input document as a graph and rank its nodes according to their score using graph-based ranking method. In this paper, we propose a method to compute importance of co-occurrence word in document and
Keyword spotting is the task of detecting keywords of interest in continuous speech. This work investigates the application of keyword spotting to detect crime and it can be used along with telephone tapping and audio monitoring devices by security organization. In this work phonetic based word spotter is developed. A
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