Formalizing Expert Knowledge for Developing Accurate Speech Recognizers

The expertise required to develop a speech recognition system with reasonable accuracy for a given task is quite significant, and precludes most non-speech experts from integrating speech recognition into their own research. While an initial baseline recognizer may readily be available or relatively simple to acquire, identifying the necessary accuracy optimizations require an expert understanding of the application domain as well as significant experience in building speech recognition systems. This paper describes our efforts and experiments in formalizing knowledge from speech experts that would help novices by automatically analyzing an acoustic context and recommending appropriate techniques for accuracy gains. Through two recognition experiments, we show that it is possible to model experts' understanding of developing accurate speech recognition systems in a rule-based knowledge base, and that this knowledge base can accurately predict successful optimization techniques for previously seen acoustic situations, both in seen and unseen datasets. We argue that such a knowledge base, once fully developed, will be of tremendous value for boosting the use of speech recognition in research and development on non-mainstream languages and acoustic conditions.