Two-level recognition of isolated word using neural nets
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This paper describes a neural-net based isolated word recogniser that has a better performance on a standard multi-speaker database than our reference Hidden Markov Model recogniser. The complete neural net recogniser is formed from two parts: a front-end which transforms the complex acoustic specification of the speech into a simplified phonetic feature specification, and a whole-word discriminator net. Each level was trained separately, thus considerably reducing the time necessary to train the overall system.
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