Statistical language modelling for large vocabulary speech recognition
McGreevy, Michael (2006) Statistical language modelling for large vocabulary speech recognition. Masters by Research thesis, Queensland University of Technology.
The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands on language models. In a large vocabulary system, acoustic confusion is greater, thus there is more reliance placed on the language model for disambiguation. In addition to this, ASR systems are increasingly being deployed in situations where the speaker is not conscious of their interaction with the system, such as in recorded meetings and surveillance scenarios. This results in more natural speech, which contains many false starts and disfluencies.
In this thesis we investigate a novel approach to the modelling of speech corrections.
We propose a syntactic model of speech corrections, and seek to determine if this model can improve on the performance of standard language modelling approaches when applied to conversational speech. We investigate a number of related variations to our basic approach and compare these approaches against the class-based N-gram.
We also investigate the modelling of styles of speech. Specifically, we investigate whether the incorporation of prior knowledge about sentence types can improve the performance of language models. We propose a sentence mixture model based on word-class N-grams, in which the sentence mixture models and the word-class membership probabilities are jointly trained. We compare this approach with word-based sentence mixture models.
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|Item Type:||QUT Thesis (Masters by Research)|
|Supervisor:||Sridharan, Subramanian & Mason, Michael|
|Keywords:||language modelling, automatic speech recognition, syntax, grammar, disfluency, sentence mixture model|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Department:||Faculty of Built Environment and Engineering|
|Institution:||Queensland University of Technology|
|Copyright Owner:||Copyright Michael McGreevy|
|Deposited On:||03 Dec 2008 04:03|
|Last Modified:||28 Oct 2011 19:48|
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