The goal of Speech Understanding Systems (SUS) is to extract meanings from a sequence of hypothetical words generated by a speech recognizer. Recently SUSs tend to rely on robust matchers to perform this task. This thesis describes a new method using classification trees acting as a robust matcher for speech understanding. Classification trees are used as a learning method to learn rules automatically from training data. This thesis investigates uses of classification trees in speech system and some general algorithms applied on classification trees. The linguistic approach requires more human time because of the overhead associated with handling a large number of rules, whereas the proposed approach eliminates the need to handcode and debug the rules. Also, this approach is highly resistant to errors by the speaker or by the speech recognizer by depending on some semantically important words rather than entire word sequence. Furthermore, by re-training classification trees on a new set of training data later, system improvement is done easily and automatically. The thesis discusses a speech understanding system built at McGill University using the DARPA-sponsored Air Travel Information System (ATIS) task as training corpus and testbed.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.27922 |
| Date | January 1997 |
| Creators | Yi, Kwan. |
| Contributors | DeMori, Renato (advisor) |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Science (School of Computer Science.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001618219, proquestno: MQ37180, Theses scanned by UMI/ProQuest. |
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