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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.
|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 (IEEE)|
|Number of pages||4|
|Publication status||Published - 1 Aug 2004|