Keyword spotting is an efficient approach for search of relevant recordings in databases of recorded unconstrained speech. Many algorithms have been proposed in the past for this problem and several techniques claim to be very efficient and accurate. Researchers have so far attempted to correctly compare their results by using standardized Receiver Operating Characteristic (ROC) curves, and per...

Current peer-to-peer (p2p) full-text keyword search techniques fall into the following categories: document-based partitioning, keyword-based partitioning, hybrid indexing, and semantic search. This paper provides a performance evaluation and comparison of these p2p full-text keyword search techniques on a dataset with 3.7 million web pages and 6.8 million search queries. Our evaluation results...

Keyword spotting deals with the search of a reduced set of keywords in audio content. Phone Lattice-based approaches are very fast but achieve poor results. HMM-based keyword spotting systems deal with filler models to absorb the Out-of-vocabulary (OOV) words and achieve best results although they are slower. We propose a technique which combines them in order to perform a confidence measure to...

Keyword search is the most popular querying technique on semistructured data. Keyword queries are simple and convenient. However, as a consequence of their imprecision, there is usually a huge number of candidate results of which only very few match the user’s intent. Unfortunately, the existing semantics for keyword queries are ad-hoc and they generally fail to “guess” the user intent. Therefo...

The task of keyword spotting is to detect a set of keywords in the input continuous speech. In a keyword spotter, not only the keywords, but also the non-keyword intervals must be modeled. For this purpose, filler (or garbage) models are used. To date, most of the keyword spotters have been based on hidden Markov models (HMM). More specifically, a set of HMM is used as garbage models. In this p...

Received Dec 20, 2016 Revised May 12, 2017 Accepted Jun 6, 2017 Stability studies of multi machine system are a major concern to power system engineers due to the increasing complexity involved. This paper deals with the application of a nonlinear technique called Dynamic Inversion, to TCSC for the improvement of stability of multi-machine system. The transient stability studies for various cas...

Now a day’s keyword search in data mining is very emerging topic. Latest keyword search techniques on Semantic Web are moving away from shallow, information retrieval-style approaches that merely find ―keyword matches‖ towards more interpretive approaches that attempt to induce structure from keyword queries. Exploiting identity links among RDF resources allows applications to efficiently integ...

This paper extends standard help system technology to demonstrate the suitability of Formal Concept Analysis in displaying, searching and navigating help content. The paper introduces a method for building suitable scales directly from the help system index by computing a keyword extension set. The keyword extension technique is generalisable in any document collection where a hand-crafted inde...