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.

Introduction: Speech-enabled natural-language question-answering interfaces to enterprise application systems, such as Incident-logging systems, Customer-support systems, Marketing-opportunities systems, Sales data systems etc., are designed to allow end-users to speak-out the problems/questions that they encounter and get automatic responses. The process of converting human spoken speech into text is performed by an Automatic Speech Recognition (ASR) engine. While functional examples of ASR with enterprise systems can be seen in day-to-day use, most of these work under constraints of a limited domain, and/or use of additional domain-specific cues to enhance the speech-to-text conversion process. Prior speech-and-natural language interfaces for such purposes have been rather restricted to either Interactive Voice Recognition (IVR) technology, or have focused on building a very specialized speech engine with domain specific terminology that recognizes key-words in that domain through an extensively customized language model, and trigger specific tasks in the enterprise application system. This makes the interface extremely specialized, rather cumbersome and non-adaptable for other domains. Further, every time a new enterprise application requires a speech and natural language interface, one has to redevelop the entire interface again. An alternative to domain-specific speech recognition engines has been to re-purpose general-purpose speech recognition engines, such as Google Speech API, IBM Watson Speech to text API which can be used across domains with natural language question answering systems. Such general-purpose automatic speech engines (gp-ASR) are deep trained on very large general corpus using deep neural network (DNN) techniques. The deep learnt acoustic and language models enhance the performance of a ASR. However, this comes with its own limitations. For freely spoken natural language sentences, the typical recognition accuracy achievable even for state-of-the-art speech recognition systems have been observed to be about 60% to 90% in real-world environments BIBREF0 . The recognition is worse if we consider factors such as domain-specific words, environmental noise, variations in accent, poor ability to express on the part of the user, or inadequate speech and language resources from the domain to train such speech recognition systems. The subsequent natural language processing, such as that in a question answering system, of such erroneously and partially recognized text becomes rather problematic, as the domain terms may be inaccurately recognized or linguistic errors may creep into the sentence. It is, hence, important to improve the accuracy of the ASR output text. In this paper, we focus on the issues of using a readily available gp-ASR and adapting its output for domain-specific natural language question answering BIBREF1 . We present two mechanisms for adaptation, namely We present the results of these two adaptation and gauge the usefulness of each mechanism. The rest of the paper is organized as follows, in Section SECREF2 we briefly describe the work done in this area which motivates our contribution. The main contribution of our work is captured in Section SECREF3 and we show the performance of our approach through experiments in Section SECREF4 . We conclude in Section SECREF5 .

Question: Which of their proposed domain adaptation methods proves best overall?
Answer: Machine learning approach