Mobile Computing

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Multimodal content language allows voice command processing of voice inputs.

Technologie

MULTIMODEL CONTENT LANGUAGE Robert Dennyson.A(Sacred Heart College,Tirupattur)

Approaches ,[object Object],[object Object]

Language Processing ,[object Object],[object Object]

Parsing ,[object Object],[object Object],[object Object]

Multimodal Subcategorization ,[object Object]

Constructional Meaning ,[object Object],[object Object]

MULTIMODAL DISCOURSE ,[object Object],[object Object],[object Object]

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The paper represents the overall design and implementation of DSP based speech recognition and text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper represent voice oriented command for converting into text. We intended to compute the entire speech processing in real time. This involves simultaneously accepting the input from the user and using software filters to analyse the data. The comparison was then to be established by using correlation and µ law companding techniques. In this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The voice command is stored in the data base with the help of the function keys. The real time input speech received is then processed in the speech recognition system where the required feature of the speech words are extracted, filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes are done to convert the received data and into text form.

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Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, keyword extraction and information retrieval. A suitable control panel is provided for the user to comfortably carry out these activities.

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In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is it self based on the combination of lexical and semantic similarity measures.

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