Adapting general-purpose speech recognition engine output for domain-specific natural language question answering
This addresses the issue of inaccurate speech-based interfaces for enterprise systems in specific domains, but it is incremental as it builds on existing methods for adaptation.
The paper tackles the problem of general-purpose speech recognition engines producing erroneous output for domain-specific words and accents, which leads to inaccurate natural language question-answering. It presents two adaptation mechanisms (evolutionary development and machine learning) to repair speech-output, improving subsequent question-answering results.
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.