This paper is devoted to developing techniques for improving learning of foreign spoken languages. It presents a general framework for evaluating student's spoken response, which is based on collecting experimental data about experts' and novices' performance and applying machine learning and knowledge management techniques for deriving evaluation rules. The related speech analysis, visualization, and student response evaluation techniques are described. An experimental course for learning tones of Standard Chinese (Mandarin) is discussed.