sentences: There are many situations in which it is desirable to be able to distinguish spontaneous speech and speech which is non-spontaneous. Examples of situations in which this problem may arise include forensic evidence situations, sorting voice-mail responses from voice-mail menus, and automatic segmentation of spontaneous responses from prepared questions. The later situation can occur if it is desired to create a database of spontaneous data from data which consists of spontaneous discourse responding to prepared prompts. This paper outlines and compares three methods for automatically classifying spontaneous and non-spontaneous speech and presents the cxperimcntal results comparing the performance of the methods. All three methods are based on an analysis of the probability distributions of prosodic fcatures extracted from the speech signal. The first method uses an expansion of the of the probability distribution in terms of the statistical moments. The second method is an application of a modifed Hellinger’s method applied to histograms of signal amplitude and other speech features. The third method is based on a measure of the nonGaussianity of the data.