Abstract:
Spoken dialogue systems, which provide automated customer services at call centers, have become more prevalent. It is time consuming to determine a set of call types for ...Show MoreMetadata
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Abstract:
Spoken dialogue systems, which provide automated customer services at call centers, have become more prevalent. It is time consuming to determine a set of call types for the dialogue system by analyzing a large volume of unstructured spoken utterances. Traditional hierarchical agglomerative clustering (HAC) algorithms can bootstrap the call types in an unsupervised way, yet the time and space complexities are huge, especially for a large data set. Based on our observation that spoken utterances containing less than ten terms are common in the spoken dialogue system, we propose an efficient HAC algorithm for short utterances. By utilizing the particular properties of short utterances, we significantly reduce both the time and the space complexities of the clustering algorithm.
Published in: Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
Date of Conference: 23-23 March 2005
Date Added to IEEE Xplore: 09 May 2005
Print ISBN:0-7803-8874-7
ISSN Information:
First Page of the Article