Automatic categorization of voicemail transcripts using stochastic language models

Abstract

This paper is about the applicability of stochastic language models to the task of categorizing voicemail message transcripts. The target categories are related to priority and content and are thus suitable for mobile messaging applications based on profiles which can be determined by users' physical and social environment. Categorization is performed by comparing the posterior probabilities of test messages under the language models of each target category. Stochastic models were selected over other lexical features because of their ability to incorporate context dependencies while their parameters are determined automatically from data. Despite the relatively small amount of training data used and given the spontaneous nature of voicemail, the models performed fairly accurately. Our experiments examine the effects that factors such as the word error rate, the n-gram order, smoothing and textual representation have on overall categorization accuracy. © Springer-Verlag Berlin Heidelberg 2004.