The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
When applied to speech, Non-negative Matrix Factorization is capable of learning a small vocabulary of words, foregoing any prior linguistic knowledge. This makes it adequate for small-scale speech applications where flexibility is of the utmost importance, e.g. assistive technology for the speech impaired. However, its performance depends on the way its inputs are represented. We propose the use...
This paper presents a novel architecture for keyword spotting in spontaneous speech, in which keyword model is trained from a small number of acoustic examples provided by a user. The word-spotting architecture relies on scoring patch feature vector sequences extracted by using sliding windows, and performing keyword
This paper deals with the contribution of Curvelet transform to generate more accurate word image descriptors for Arabic keyword spotting in ancient documents. Due to its properties, Curvelets can tolerate more scale distortions and more directional features in images. The process of Curvelet descriptor generation is
systems to find keywords that match the submitted queries approximately. Our work focuses on constructing a flexible secure index that allows the cloud server to perform the approximate search operations without revealing the content of the query trapdoor or the index content. Specifically, the most recently cryptographic
This paper presents a new technique for preparing word templates to improve the performance of dynamic time warping based keyword spotting. The proposed technique selects one reference template from a small set of examples and in contrast to existing model based approaches does not require extensive training
As the amount of data increases and the relations among them get more complex, access to information implicit in data appears more difficult, and the role of methods of getting data from diverse texts, and analyzing them becomes more significant. Of such methods is the highly effective technique of keyword extraction
Spotting keywords in handwritten documents without transcription is a valuable method as it allows one to search, index, and classify such documents. In this paper we show that keyword spotting based on bi-directional Long Short-Term Memory (BLSTM) recurrent neural nets can successfully be applied on online
Textual web pages dominate web search engines nowadays. However, there is also a striking increase of structured data on the web. Efficient keyword query processing on structured data has attracted enough attention, but effective query understanding has yet to be investigated. In this paper, we focus on the problem of
a null score to any keyword that was not part of the training data, i.e. Out-of-Vocabulary (OOV) keywords, whereas other techniques are able to estimate a reasonable score even for these kind of keywords. We present a smoothing technique which estimates the score of an OOV keyword based on the scores of similar
keyword-based search technologies. At last, we propose a novel color-based keyword search scheme for encrypted office document. Two vectors on behalf of the keywords and the corresponding color are used. The scheme can return top-k set as correct as possible meeting user's requirements.
access control must be provided. A common operation on the data is keyword search. Currently, search operation over encrypted search is performed at the cloud servers and access control for the in-cloud data is usually enforced by users. Separation of the two types of operations can lead to reduced efficiency and
Cloud computing is emerging as a revolutionary computing paradigm which provides a flexible and economic strategy for data management and resource sharing. Security and privacy become major concerns in the cloud scenario, for which Searchable Encryption (SE) technology is proposed to support efficient keyword based
Choosing descriptive keywords to best describe digital media content is crucial for many applications, especially those involving content-based indexing or retrieval. Traditionally such keywords are selected manually, which is labor intensive, restrictive to a limited set of words and inherently subjective to the
In this paper we present our current work on automatic speaker recognition using keyword-conditioned phone N-gram modeling. We propose the use of contextual information around keywords in modeling a speaker's pronunciation characteristics at a phonetic level. Our approach is to add time margins around keywords when
Fuzzy keyword search is an important and necessary functionality for information retrieval in modern cloud storage services, since cloud users may submit queries with typos errors or have deficient knowledge about the underlying keywords of cloud data sets. However, for the purpose of privacy preservation, data is
Cloud computing becomes increasingly popular. To protect data privacy, sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. The existing searchable encryption schemes support only exact or fuzzy keyword search
cloud, this paper proposes a method to improve the systems usability and users satisfactory degree in the fuzzy keyword search field. In this paper, based on [1] and [2], for the first time we solve the problem of full-scale fuzzy keyword set construction according to the input keyword, and construct the feedback scheme to
Enabling keyword search directly over encrypted data is a desirable technique for effective utilization of encrypted data outsourced to the cloud. Existing solutions provide multi-keyword exact search that does not tolerate keyword spelling error, or single keyword fuzzy search that tolerates typos to certain extent
approach involves the detection and use of self-defining features that are available within the data. We take into account two emotionally rich features: a) emoticons and b) lists of emotionally intense keywords. These features are evaluated on data coming from a popular forum, using various classifiers and feature vectors
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.