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Neural Network Models of Sensory Integration for Improved Vowel Recognition

by Ben P. Yuhas, Moise H. Goldstein, Terrence J. Sejnowski, Robert E. Jenkins
Proceedings of the IEEE ()
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It is demonstrated that multiple sources of speech information can\nbe integrated at a subsymbolic level to improve vowel recognition.\nFeedforward and recurrent neural networks are trained to estimate the\nacoustic characteristics of a vocal tract from images of the speaker's\nmouth. These estimates are then combined with the noise-degraded\nacoustic information, effectively increasing the signal-to-noise ratio\nand improving the recognition of these noise-degraded signals.\nAlternative symbolic strategies such as direct categorization of the\nvisual signals into vowels are also presented. The performances of these\nneural networks compare favorably with human performance and with other\npattern-matching and estimation techniques

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