General purpose automatic speech recognition
(gpASR) systems such as Google, Watson, etc.
sometimes output inaccurate sentences when used
in a domain specific scenario as it may not have had
enough training samples for that particular domain
and context. Further, the accent of the speaker and
the environmental conditions in which the speaker
speaks a sentence may influence the speech engine
to recognize certain words inaccurately. Many approaches
to improve the accuracy of ASR output
exist. However, in the context of a domain and
the environment in which a speaker speaks the sentences,
gpASR output needs a lot of improvement
in order to provide effective speech interfaces to
domain-specific systems. In this paper, we demonstrate
a method that combines bio-inspired artifi-
cial development (ArtDev) with machine learning
(ML) approaches to repair the output of a gpASR.
Our method factors in the environment to tailor the
repair process.
Business application systems traditionally have menu-driven interfaces (whether stand-alone or web-enabled etc.) that users operate on. However, such an interface can become rather cumbersome for users who want some data from the system, but do not know how to get it. Natural language based user-interface to business applications is one alternative. Further, as email and SMS based interactions becomes more ubiquitous, future business application systems may enable email and SMS based interfaces to their systems. This would entail a natural language interface to business applications. We describe a framework for text-based natural language conversational user-interface, for business applications. Our framework permits the user to carry out a dialog with the system in order to fetch relevant data and carry out various tasks of the system. The framework uses semantic web based ontology of the domain, to aid in the retrieval of the relevant data and concepts from the system.
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