Abstract
The recognition of spoken letters of the alphabet by machine has been long accepted as a challenging and important problem, and has been viewed as a benchmark for automatic speech recognition [1]. The alphabet vocabulary contains several highly confusable subsets such as the ‘E-set’ (‘b’, ‘c’, ‘d’, ‘e’, ‘g’, ‘p’, ‘t’ ‘v’), the ‘A-set’ (‘a’, ‘j’,’ k’), and the pair’ m’ and ‘n’. Not surprisingly, most of the recognition errors occur due to the misidentification of one of the members of these subsets. It is clear that an effective algorithm for alphabet recognition must exploit all the available discriminatory information present in the speech signal.
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© 1992 British Telecommunications plc
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Woodland, P.C. (1992). Spoken Alphabet Recognition Using Multilayer Perceptrons. In: Linggard, R., Myers, D.J., Nightingale, C. (eds) Neural Networks for Vision, Speech and Natural Language. BT Telecommunications Series, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-2360-0_10
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DOI: https://doi.org/10.1007/978-94-011-2360-0_10
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