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Abstract
This paper explores the issues involved in using symbolic metric algorithms for automatic speech recognition (ASR), via a structural representation of speech. This representation is based on a set of phonological distinctive features which is a linguistically well-motivated alternative to the ``beads-on-a-string'' view of speech that is standard in current ASR systems. We report the promising results of phoneme classification experiments conducted on a standard continuous speech task.
Original language | English |
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Title of host publication | Proceedings of the 17th International Conference on Pattern Recognition, 2004 (ICPR 2004) |
Place of Publication | Cambridge, UK |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 438-441 |
Number of pages | 4 |
Volume | 3 |
ISBN (Print) | 0769521282 |
DOIs | |
Publication status | Published - 1 Aug 2004 |
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