Repairing ASR Output by Artificial Development and Ontology Based Learning

Abstract

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.