Accurate detection of emotion from speech has clear benefits for the design of more natural human-machine speech interfaces or for the extraction of useful information from large quantities of speech data. The task consists of assigning, out of a fixed set, an emotion category, e.g., anger, fear, or satisfaction, to a speech utterance. In recent work, several classifiers have been proposed for automatic detection of a speaker's emotion using spoken words as the input. These classifiers were designed independently and tested on separate corpora, making it difficult to compare their performance. This paper presents three classifiers, two popular classifiers from the literature modeling the word content via n-gram sequences, one based on an interpolated language model, another on a mutual information-based feature-selection approach, and compares them with a discriminant kernel-based technique that we recently adopted. We have implemented these three classification algorithms and evaluated their performance by applying them to a corpus collected from a spoken-dialog system that was widely deployed across the US. The results show that our kernel-based classifier achieves an accuracy of 80.6%, and out-performs both the interpolated language model classifier, which achieved a classification accuracy of 70.1%, and the classifier using mutual information-based feature selection (78.8%).