Speech act classification : A comparison of algorithms for classifying out of context utterances with DAMSL

Sammanfattning: With the growing of everyday automation the need for better speech understanding in machines increases. A unsolved problem in speech processing is the automatic recognition of speech acts. A speech act is a utterance which fills a function in the communication. This problem is approached in this thesis by fitting classifiers using machine learning algorithms. The algorithms used are Linear Support Vector Classifier, Multinomial Naive Bayes, Decision Tree, and Perceptron. The N-gram model was used in combination with a tf-idf to extract features. Utterances are used out of context for the tests. None of the algorithms reaches over 30% accuracy but gets more than twice that as F1 score. The Decision Tree classifier was as expected the fastest but the SVC had the overall highest scores.