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Machine Learning for Adaptive Spoken Control in PDA Applications
Date Issued
12 October 2003
Date Available
10T09:15:07Z July 2013
Abstract
A machine learning approach to interpreting utterances in spoken
interfaces is described, where evidence from the utterance and from the dialogue
context is combined to estimate a probability distribution over interpretations.
The algorithm for the utterance evidence uses nearest-neighbour classification on
a set of training examples, while the contextual evidence is provided by dialogue
act n-grams derived from dialogue corpora. Each algorithm can adapt by
recording data from the user at hand. Experimental results for the utterance
interpreter show that adaptation to a particular user’s training utterances
significantly improves recognition accuracy over training on utterances from the general population.
Type of Material
Conference Publication
Keywords
Language
English
Status of Item
Peer reviewed
Conference Details
Artificial Intelligence in Mobile Systems 2003 Workshop (AIMS 2003), 12th October, 2003, Seattle, USA, in conjunction with 5th International Conference on Ubiquitous Computing
This item is made available under a Creative Commons License
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