# Adapting general-purpose speech recognition engine output for domain-specific natural language question answering Source: [Adapting general-purpose speech recognition engine output for domain-specific natural language question answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:47:17.894Z Authors: C. Anantaram, Sunil Kumar Kopparapu Published: 2017-10-12T12:18:16.000Z ## Abstract Speech-based natural language question-answering interfaces to enterprise systems are gaining a lot of attention. General-purpose speech engines can be integrated with NLP systems to provide such interfaces. Usually, general-purpose speech engines are trained on large \`general' corpus. However, when such engines are used for specific domains, they may not recognize domain-specific words well, and may produce erroneous output. Further, the accent and the environmental conditions in which the speaker speaks a sentence may induce the speech engine to inaccurately recognize certain words. The subsequent natural language question-answering does not produce the requisite results as the question does not accurately represent what the speaker intended. Thus, the speech engine's output may need to be adapted for a domain before further natural language processing is carried out. We present two mechanisms for such an adaptation, one based on evolutionary development and the other based on machine learning, and show how we can repair the speech-output to make the subsequent natural language question-answering better. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()