Design and comparison of several statistical approaches to Speech Understanding in two different tasks and languages

In this paper, a study of different approaches to the problem of speech understanding in restricted semantic domains is presented. Two systems based on generative models are proposed and they are compared with a system based on discriminative methods. The experiments were conducted on two different tasks, DIHANA and MEDIA, which are in two different languages. The use of the two tasks is of interest not only because of the differences in how concepts are expressed in both languages, but also because of the differences in the way of representing the semantics. The results show the ability of automatically learned statistical models to represent the semantics, even when dealing with voice input, which introduces errors that are generated in the recognition process.