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Speech act classification: A comparison of algorithms for classifying out of context utterances with DAMSL
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. , p. 18
Series
UMNAD ; 1126
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-142512OAI: oai:DiVA.org:umu-142512DiVA, id: diva2:1161780
Educational program
Bachelor of Science Programme in Computing Science
Supervisors
Examiners
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2017-12-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Language
  • de-DE
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Output format
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