Speech utterance categorisation given one training utterance per category
Speech utterance categorisation given one training utterance per category
- Author(s): A. Albalate and D. Suendermann
- DOI: 10.1049/cp:20081180
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- Author(s): A. Albalate and D. Suendermann Source: 4th International Conference on Intelligent Environments (IE 08), 2008 page ()
- Conference: 4th International Conference on Intelligent Environments (IE 08)
- DOI: 10.1049/cp:20081180
- ISBN: 978 0 86341 894 5
- Location: Seattle, WA, USA
- Conference date: 21-22 July 2008
- Format: PDF
In this paper, we address the categorisation of speech utterances within the scenario of technical support automated agents given only one labelled utterance per category . The categorisation algorithm maps input utterances into bag-of-word vectors and then applies feature extraction based on soft word clustering. We analyse two feature extraction schemes: pole-based overlapping clustering (PoBOC) and a combination of PoBOC with Fuzzy c-medoids. For the categorisation at the utterance level, we use the Nearest Neighbour (NN) approach. Finally, we evaluate the proposed methods on a test corpus with more than 3000 utterances recorded in a commercial dialog system. (4 pages)
Inspec keywords: pattern clustering; speech processing; fuzzy set theory
Subjects: Combinatorial mathematics; Combinatorial mathematics; Speech and audio signal processing; Speech processing techniques
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