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Context Dependent Probability Adaptation in Speech Understanding in the Philips Automatic Inquiry System

Drenth, E. (1996) Context Dependent Probability Adaptation in Speech Understanding in the Philips Automatic Inquiry System. Master's Thesis / Essay, Artificial Intelligence.

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In this thesis, we will look at ways to improve speech understanding using dialogue context information (i.e. the past dialogue history) in thePhilips Automatic Inquiry System. Several different methods of language modelling will be discussed: bigrams, trigrams and concept set estimations. Combinations of these models will also be investigated, as well as different ways for modelling context information. We will see that context dependence can bring some remarkable improvements, especially when taking into account the circumstances we will have to deal with. In chapter 2, a general description of the system will be given, chapters 3 and 4 will be concerned with system adaptations and data setup. In chapter 5, we will define contexts manually, and in chapter 6 we will use these contexts to train context dependent bigram models. In chapter 7 we will again use these contexts for training what we will call graph based models: We will estimate joint probabilities of concepts using graph theory. In chapter 8 we will train bigram models again, only then we will use contexts that are selected automatically, by a clustering algorithm. Some previous results of a similar investigation into context dependencies will be discussed in chapter 9, and we will also try to reproduce these results. Finally, we will state our conclusions in chapter 10. Regretfully, there will be no room to perform all experiments to our complete satisfaction, as not all methods at our disposal are particularly suited for the system we work with. We are also constrained in our conclusions, due to the lack of data. So some questions will be left unanswered. Still, this thesis will be a thorough survey of the possibilities there are to model context dependencies in this particular system, and many conclusions will also hold for similar systems.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30

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